diff --git "a/-NE1T4oBgHgl3EQf8QVm/content/tmp_files/load_file.txt" "b/-NE1T4oBgHgl3EQf8QVm/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/-NE1T4oBgHgl3EQf8QVm/content/tmp_files/load_file.txt" @@ -0,0 +1,942 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf,len=941 +page_content='1 Efficient Mutation Testing via Pre-Trained Language Models Ahmed Khanfir , Renzo Degiovanni , Mike Papadakis and Yves Le Traon SnT, University of Luxembourg, Luxembourg Abstract—Mutation testing is an established fault-based testing technique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' It operates by seeding faults into the programs under test and asking developers to write tests that reveal these faults.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' These tests have the potential to reveal a large number of faults – those that couple with the seeded ones – and thus are deemed important.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To this end, mutation testing should seed faults that are both “natural” in a sense easily understood by developers and strong (have high chances to reveal faults).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To achieve this we propose using pre-trained generative language models (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' CodeBERT) that have the ability to produce developer-like code that operates similarly, but not exactly, as the target code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This means that the models have the ability to seed natural faults, thereby offering opportunities to perform mutation testing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We realise this idea by implementing µBERT, a mutation testing technique that performs mutation testing using CodeBert and empirically evaluated it using 689 faulty program versions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Our results show that the fault revelation ability of µBERT is higher than that of a state-of-the-art mutation testing (PiTest), yielding tests that have up to 17% higher fault detection potential than that of PiTest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Moreover, we observe that µBERT can complement PiTest, being able to detect 47 bugs missed by PiTest, while at the same time, PiTest can find 13 bugs missed by µBERT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Index Terms—Fault Injection, Mutation Testing, Pre-Trained Language Models !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 1 INTRODUCTION Mutation testing aims at seeding faults using simple syntac- tic transformations [19].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' These transformations, also known as mutation operators are typically constructed based on syntactic rules crafted based on the grammar of the target programming language [8], i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' replacing an arithmetic op- erator with another such as a + by a -.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Unfortunately, such techniques generate mutants (seeded faults), many of which are “unatural”, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', non-conforming to the way developers code, thereby perceived as unrealistic by developers [11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' At the same time, the syntactic-based fault seeding fails to capture the semantics of the code snippets that they apply, leading to numerous trivial or low utility faults [46].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To deal with the above issue we propose forming natural mutations by using big code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Thus, we aim at introducing modifications that follow the implicit rules, norms and cod- ing conventions followed by programmers, by leveraging the capabilities of pre-trained language models to capture the underlying distribution of code and its writing, as learned by the pre-training process on big code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To this end, we rely on CodeBERT [22], an NL-PL bimodal language model that has been trained on over 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4 million programs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' More precisely, we use its Masking Modelling Language (MLM) functionality, which given a code sequence with a masked token, predicts alternative replacements to that token, that is best matching the se- quence context.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This is important, since the predictions do not follow fixed predefined patterns as is the case of conventional mutation testing, but are instead adapted to fit best the target code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For instance, given a sequence A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Khanfir, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Degiovanni, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Papadakis, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Le Traon are with the University of Luxembourg, Luxembourg.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' int a = 1;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', we pass a masked version of it as int a = ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', then CodeBERT by default proposes 5 predictions sorted by likelihood score: 0, 1, b, 2, and 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Being the most likely fitting tokens to the code context, our intuition is that replacing the masked token with these predictions would induce “natural” mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Precisely, we introduce µBERT, a mutation testing ap- proach that uses a pre-trained language model (CodeBERT) to generate mutants by masking and replacing tokens with the aim of forming natural mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' µBERT iterates through the program statements and modifies their token.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In par- ticular, µBERT proceeds as follows: (1) it selects and masks one token at a time;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (2) feeds CodeBERT with the masked sequence and obtains the predictions;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (3) creates mutants by replacing the masked token with the predicted ones;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' and (4) discards non-compilable, duplicate and equivalent mutants (mutants syntactically equal to original code).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Recent research [32] has shown that some real faults are only captured by using complex patterns, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' patterns that require more than one token mutation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To account for such cases, µBERT is equipped with additive mutations, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', mutations that add code (instead of deleting or altering).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For example, consider a boolean expression e1 (typically present in if, do, while and return statements), which is mutated by µBERT by adding a new condition e2, thereby generating a new condition e1||e2 (or e1&&e2), which is then masked and completed by CodeBERT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For instance, given a condi- tion if(a == b), µBERT produces a new condition if(a == b || a > 0) that is masked and produces if(a == b || b > 0).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We implement µBERT, and evaluate its ability to serve the main purposes of mutation testing, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' guiding the testing towards finding faults.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We thus, evaluate it using 689 faults from Defects4J and asses µBERT effectiveness and arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='03543v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='SE] 9 Jan 2023 2 cost-efficiency to reveal1 them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Our results show that µBERT is very effective in terms of fault revelation, finding on average 84% of the faults.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This implies that µBERT mutants cover efficiently faulty behaviours caused by real bugs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' More importantly, the approach is noticeably more effec- tive and cost-efficient than a traditional mutation testing technique, namely PiTest [17], that we use as a baseline in our evaluation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Precisely, we consider three different configurations for PiTest that uses different sets of mutation operators (DEFAULT, ALL and RV).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In fact, test suites that kill all mutants of µBERT find on average between 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5% to 33% more faults than those generated to kill all mutants introduced by PiTest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Moreover, even when analysing the same number of mutants, µBERT induces test suites that find on average 6% to 16% more faults than PiTest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' These results are promising and endorse the usage of µBERT over the considered mutation testing technique, as a test generation and assessment criterion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We also study the impact of the condition-seeding-based mutations in the fault detection capability of µBERT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We observe that test-suites designed to kill both kinds of µBERT mutants – induced by 1) direct CodeBERT predictions and 2) a combination of conditions-seeding with CodeBERT predictions – find on average over 9% more bugs than the ones designed to kill direct CodeBERT prediction mutants only (1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Overall, our main contributions are: We introduce µBERT, the first mutation testing ap- proach that uses pre-trained language models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' It lever- ages the model’s code knowledge captured during its pretraining on large code corpora and its ability to capture the program context, to produce “natural” mu- tants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We propose new additive mutations which operate by seeding new conditions in the existing conditional expressions of the target code, then masking and re- placing their tokens with the model predictions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We provide empirical evidence that µBERT mutants can guide testing towards higher fault detection capabili- ties, outperforming those achieved by SOA techniques (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' PiTest), in terms of effectiveness and cost-efficiency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In our empirical study, we validate also the advantage of employing the new additive mutation patterns, w.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='t improving the effectiveness and cost-efficiency in writ- ing test suites with higher fault revelation capability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 2 BACKGROUND 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='1 Mutation Testing Mutation analysis [47] is a test adequacy criterion repre- senting test requirements by the mean of mutants, which are obtained by performing slight syntactic modifications to the original program.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For instance, an expression like x > 0 can be mutated to x < 0 by replacing the relational operator > with <.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' These mutants are then used to assess the effectiveness and thoroughness of a test suite in detecting their corresponding code modification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Tests are written/generated to kill (reveal) the mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' A bug is revealed by a mutation testing approach, if the written tests to kill its mutants also reveal the bug.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' A test case detects a mutant if it is capable of producing distinguishable observable outputs between the mutant and the original program.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' A mutant is said to be killed if it is detected by a test case or a test suite;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' otherwise, it is called live or survived.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Some mutants cannot be killed as they are functionally equivalent to the original program.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The mutation score measures the test suite adequacy and is computed as the ratio of killed mutants over the total number of generated mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='2 Generative Language Models Advances in deep learning approaches gave birth to new language models for code generation [1], [4], [15], [22].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' These models are trained on large corpora counting multiple projects, thereby acquiring a decent knowledge of code, enabling them to predict accurately source code to devel- opers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Among these pre-trained models, CodeBERT [22], a language model that has been recently introduced and made openly accessible for researchers by Microsoft.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' CodeBERT is an NL-PL bimodal pre-trained language model (Natural Language Programming Language) that supports multiple applications such as code search, code documentation generation, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Same as most large pre- trained models, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' BERT [20], CodeBERT’s developing adopts a Multilayer Transformer [55] architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' It has been trained on a large corpus collected from over 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4 million projects available on GitHub, counting 6 different programming languages, including Java.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The model was trained in a cross-modal fashion, through bimodal NL-PL data, where the input data is formed by pairs of source code and its related documentation, as well-as unimodal data, including either natural language or programming language sequences per input.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This way, it enables the model to offer both – PL and NL-PL – functionalities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The training targets a hybrid objective function, that is based on replaced token detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' µBERT incorporates the Masked Language Modeling (MLM) functionality [2] of CodeBERT in its workflow, to generate “natural” mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The CodeBERT MLM pipeline takes as input a code sequence of maximum 512 tokens, including among them one masked as , whose value will be predicted by the model based on the context captured from the remaining tokens.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' CodeBERT provides by default 5 predictions per token, among which we use the inaccurate and compilable predicted codes as mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3 APPROACH We propose µBERT, a generative language-model-based mu- tation testing approach, which is described step by step in Figure 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Given an input source code, µBERT leverages CodeBERT’s knowledge of code and its capability in captur- ing the program’s context to produce “natural” mutations, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' that are similar to eventual developer mistakes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='To do so, µBERT proceeds as follows in six steps: 1) First, it extracts relevant locations (AST 2 nodes) where to mutate 2) Second, it masks the identified node-tokens, creating one masked version per selected token.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' AST: Abstract Syntax Tree.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3 if (a !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='= b) return a !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='= d || b>0;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Source-code AST nodes locations 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' AST Nodes selection Y Y 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Masked code prediction Model Predictions Predict° 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Conditions seeding 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Injection & Compilation check Injected faults if (a !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='= b) return a !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='= d;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' if (a !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='= b && b>i) return a !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='= d;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' a = b + c;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' return a d;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' AST Nodes Masking a = b + c;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' return a == d;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' a = + c;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' return a == d;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 1: µBERT Workflow: (1) it parses the Java code given as input, and extracts the expressions to mutate;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (2) it creates simple-replacement mutants by masking the tokens of interest and invoking CodeBERT;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (3) it generates the mutants by replacing the masked token with CodeBERT predictions;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (4) it generates complex mutants via a) conditions-seeding, b) tokens masking then c) replacing by CodeBERT predictions;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' and finally, (5) it discards not compiling and syntactically identical mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3) Then, it invokes CodeBERT to predict replacements for these masked tokens.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 4) In addition to the mutants produced in Step (3), µBERT also implements some condition-seeding additive mu- tations that modify more than one token.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Precisely, it modifies the conditional expressions in the control flow (typically present in if, do, while and return state- ments) by extending the original condition with a new one, combined with the logical operator && or ||.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then, the new conditional expression is mutated by following the same steps (2) and (3) – masking and replacing the masked tokens by the CodeBERT predictions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 5) Finally, the approach discards duplicate predictions or those inducing similar code to the original one, or not compiling, and outputs the remaining ones as mutants, from diverse locations of the target code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' More precisely, it iterates through the statements in random order and outputs in every iteration one mutant per line, until achieving the desired number of mutants or all mutants are outputted.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='1 AST Nodes Selection µBERT parses the AST of the input source code and selects the lines that are more likely to carry the program’s specifi- cation implementation, excluding the import statements and the declaration ones, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' the statements declaring a class, a method, an attribute, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This way, the approach focuses the mutation on the business-logic portion of the program and excludes the lines that are probably of lower impact on the program behaviour.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' It proceeds then, by selecting from each of these statements, the relevant nodes to mutate, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' the operators, the operands, the method calls and variables, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', and excluding the language-specific ones, like the separators and the flow controls, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' semicolons, brackets, if, else, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Table 1 summarises the type of targeted AST nodes by µBERT, with corresponding example expressions and induced mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We refer to these as the conventional mutations provided by µBERT, denoted by µBERTconv in our evaluation, previously introduced in the preliminary version of the approach [18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='2 Token Masking In this step, we mask the selected nodes one by one, pro- ducing a masked version from the original source code for each node of interest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This means that every masked version contains the original code with one missing node, replaced by the placeholder .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This way, µBERT can generate several mutants in the same program location.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For instance, for an assignment ex- pression like res = a + b, µBERT will create (potentially 25) mutants from the following masked sequences: = a + b res = a + b res = + b res = a b res = a + 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='3 CodeBERT-MLM prediction µBERT invokes CodeBERT to predict replacements for the masked nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To do so, it tokenizes every masked version into a tokens vector then crops it to a subset one that fits the maximum size allowed by the model (512) and counts the masked token with the surrounding code-tokens.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Next, our approach feeds these vectors to CodeBERT MLM to predict the most probable replacements of the masked token.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Our intuition is that the larger the code portion accompanying the mask placeholder, the better CodeBERT would be able to capture the code context, and consequently, the more meaningful its predictions would be.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This step ends with the generation of five predictions per masked token.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4 Condition seeding µBERT generates second-order mutants by combining con- dition seeding with CodeBERT prediction capabilities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To do so, our approach modifies the conditions in control flow and ava4 TABLE 1: Example of µBERT conventional mutations, available in the preliminary version of the approach [18], denoted by µBERTconv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Ast node Expression Masked Expression Mutant Example literals res + 10 res + res + 0 identifiers res + 10 + 10 a + 10 binary expressions a && b a b a || b unary expressions --a a ++a assignments sum += current sum = current sum -= current object fields node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='next node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' node.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='prev method calls list.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='add(node) list.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='(node) list.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='push(node) array access arr[index + 1] arr[] arr[index] static type references Math.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='random() * 10 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='random() * 10 Random.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='random() * 10 return statements, including if, do, while and return conditional expressions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For every one of these statements, it starts by extending the original condition by a new one, separated with the logical operator && or ||, in both orders (original condition first or the other way around) and with or without negation (!' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Next, all substitute conditions are put one by one in place in the original code, forming multiple condition-seeded code versions, that we pass as input to Step (2), in which their tokens are masked and then (3) passed each to Code- BERT to predict the best substitute of their corresponding masked tokens.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The seeded conditions are created in two ways: 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='1 Using existing conditions in the same class To mutate a given condition – if, do, while and return conditional expressions –, we collect all other conditions existing in the same class, then combine each one of them with the target condition, using logical operators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Precisely, let Expt a conditional expression to mutate and SE = {Exp0, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', Expn} the set of other conditional expressions appearing in the same class, excluding the null- check ones (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' var == null).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The alternative replacement conditions generated for Expt are the combinations of: Expt op neg Expi and Expi op neg Expt, where op is a binary logical operator taking the values in {&&,||}, neg is either the negation operator !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' or nothing and Expi is a condition from SE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='2 Using existing variables in the same class When the target if conditional expression to mutate con- tains variables (including fields), we create new additional conditions by combining these variables with others of the same type from the same class.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then we combine each one of the newly created conditions with the original one, using logical operators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Precisely, let Expt be a conditional expression to mutate containing a set of variables Svt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For every variable vart in Svt, we load Sv = {var0, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', varn} the set of other variables appearing in the same class and of the same type T as vart, then we generate the following new conditions: Expt op (vart relop vari) and (vart relop vari) op Expt, where op is a binary logical operator taking the values in {&&,||}, relop is a relational operator applicable on the type T and vari is a variable from Sv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5 Mutant filtering In this step, our approach starts by discarding accurate and duplicate predictions;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' the redundant predictions and the ones that are exactly the same as the original code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then, it iterates through the statements and selects in every iteration one compilable prediction by line, while discarding not compilable ones.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Once all first-order mutants are selected (issued by one single token replacement), our approach proceeds by selecting second-order ones (issued by the com- bination of condition seeding and one token replacement) in the same iterative manner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' µBERT continues iterating until achieving the desired number of mutants or all mutants are outputted.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 4 RESEARCH QUESTIONS We start our analysis by investigating the advantage brought by the additive mutations (a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='k.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' conditions seeding ones) w.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' the fault detection capabilities of test suites designed to kill µBERT’s mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Thus, we ask: RQ1 (µBERT Additive mutations) What is the added value of the additive mutations on the fault detection capabili- ties of test suites designed to kill µBERT’s mutants?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To answer this question, we generate two sets of mutants using µBERT: 1) the first set using all possible mutations that we denote as µBERT and 2) a second one using only the conventional µBERT’ mutations – part of our preliminary implementation [18], excluding the additive ones – that we denote as µBERTconv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then we evaluate the fault detection ability of test suites selected to kill the mutants from each set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The answer of this question provides evidence that the additive mutations increase the fault detection capability of µBERT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Yet, to assess its general performance we compare it to state-of-the-art (SOA) mutation testing, particularly PiTest [17], and thus, we ask: RQ2 (Fault detection) How does µBERT compare with state- of-the-art mutation testing, in terms of fault detection?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To answer this question we generate mutants using the latest version of PiTest [17], on the same target projects as for RQ1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' As we are interested in comparing the approaches and not the implementations of the tools, we exclude the subjects on which PiTest did not run correctly or did not generate any mutant.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This way we ensure having a fair base of comparison by counting exactly the same study subjects for both approaches (further details are given in Section 5).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then, we compare the fault detection capability of test suites 5 selected to kill the same number of mutants produced by each approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Finally, we qualitatively analyse some of the mutants generated with µBERT and ask: RQ3 (Qualitative analysis) Does µBERT generate different mutants than traditional mutation testing operators?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To answer this question, we showcase the mutants gen- erated by µBERT that help in detecting faults not found by PiTest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Additionally, we discuss the program-context- capturing importance in µBERT’s functioning, by rerunning it with a reduced size of the masked codes passed to the model, and comparing examples of yielded mutants with those obtained in our original setup.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 5 EXPERIMENTAL SETUP 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='1 Dataset & Benchmark To evaluate µBERT’s fault detection, we use real bugs from a popular dataset in the software engineering research area – Defects4J [29] v2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In this benchmark, every subject bug is provided with a buggy version of the source code, its corresponding fixed version, and equipped with a test suite that passes on the fixed version and fails with at least one test on the buggy one.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The dataset includes over 800 bugs from which, we exclude the ones presenting issues, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' with wrong revision ids, not compiling or with execution issues, or having failing tests on the fixed version, at the reporting time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Next, we run µBERT and PiTest on the corresponding classes impacted by the bug from the fixed versions of the remaining bugs and exclude the ones where no tool generated any mutant, ending up with 689 bugs covered by µBERT and 457 covered by PitTest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' As we’re interested in comparing the approaches and not the tools’ implementations, and to exclude eventual threats related to the environment (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' supported java and juint versions by each technique, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=') or the limitations and shortages of the dataset, we establish every comparison study on a dataset counting only bugs covered by all considered approaches: 689 bugs to answer RQ1 and 457 to answer RQ2 and RQ3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='2 Experimental Procedure To assess the complementary and added value in terms of fault revelation of the condition-seeding-based mutations (answer to RQ1), we run our approach with and without those additional mutations – that we name respectively µBERT and µBERTconv–, and thus, generating all possible mutants on our dataset programs’ fixed versions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Next, we compare the average effectiveness of the test suites generated to kill the mutants of each set;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' induced by µBERT and µBERTconv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Once the added value of the proposed condition- seeding-based mutations is validated, we compare its per- formance to S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' mutation testing (answer to RQ2 and RQ3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We use PiTest [17], a stable and mature Java mutation testing tool, because it has been more effective at finding faults than other tools [33] and it is among the most com- monly used by researchers and practitioners [47], [52], as of today.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The tool proposes different configurations to adapt the produced mutations and their general cost to the target users, by excluding or including mutators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Among these configurations we used the three following: Pit-all (ALL) which counts all available mutation oper- ators available in the current version3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Pit-default (DEFAULTS) whose mutators are selected to form a stable and cost-efficient subset of operators by producing less but more relevant mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Pit-rv-all (ALL) which is a version4 that includes the mutators of Pit-all and extra experimental [7] ones that are made available for research studies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To compare the different approaches, we evaluate their effectiveness and cost-efficiency in achieving one of the main purposes of mutation testing, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', to guide the testing towards higher fault detection capabilities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For this reason, we simulate a mutation testing use-case scenario, where a developer/tester selects mutants and writes tests to kill them [13], [34].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We run every approach on the fixed versions and test suites provided by Defects4J, then collect the mutants and their test execution results;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' whether the mutant is killed (breaks at least one test of the test suite) and if yes by which tests.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Next, we suppose that the not killed mutants are equivalent or irrelevant, explaining why no tests have been written to kill them by the developers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then, we simulate the scenario of a developer testing the fixed version, in a state where 1) it did not have any test 2) thus all mutants did not have killing tests and 3) the developer had no knowledge of which mutants are equivalent or not.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This way, we can reproduce the developer flow of 1) selecting and analysing one mutant, 2) to either (a) discard it from the mutant set if it is equivalent (not killed in the actual test suite) or (b) write a test to kill it (by selecting one of the actual killing tests of the mutant), 3) then discarding all killed mutants by that test and 4) iterating similarly over the remaining mutants until all of them are analysed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We say that a bug is found by a mutation testing technique if the resulting test suite – formed by the written (selected) tests by the developer – contains at least one test that reveals it;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' a test that breaks when executed on the buggy version.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We express the testing cost in terms of mutants analysed, and hence, we consider the effort required to find a bug as the number of mutants analysed until the first bug-revealing test is written.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To set a common basis of comparison be- tween the approaches, accounting for the different number of generated mutants, we run the simulations until the same maximum effort is reached (maximum number of mutants to analyse), which we set to the least cost required to kill all the mutants by one of the compared approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' During our evaluation study, we use the same mutation selection strategy for all compared approaches, iterating through the lines in random order and selecting 1 arbitrary mutant per line per iteration.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To reduce the process randomness impact 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Version 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4 available in PitTest’s [6] GitHub repository (branch=master, repo=https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='com/hcoles/pitest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='git, rev- id=17e1eecf) 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Version 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4 available in PitTest’s [6] GitHub repository (branch=master, repo=https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='com/hcoles/pitest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='git, rev- id=2ec1178a) 6 on our results (in the selection of mutants and tests), we run every simulation 100 times, then average their results for every target-bug and considered approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Finally, we aggregate these averages computed on all target bugs and normalise them as global percentages of achieved fault detection by spent effort, in terms of mutants analysed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Finally, to answer RQ3, we select example mutants that enabled µBERT to find bugs exclusively (not found by any of PiTest versions), from the results of RQ2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then we discuss the added value of µBERT mutations through the analysis of the mutants’ behavioural difference from the fixed version and similarity with the buggy one.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='3 Implementation We implemented µBERT’s approach as described in Sec- tion 3: we have used Spoon [51] and Jdt [21] libraries to parse and extract the business logic related AST nodes and apply condition-seeding mutators.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To predict the masked tokens we have used the implementation proposed by CodeBERT-nt [3], [31], using CodeBERT Masked Language Modeling (MLM) functionality [2], [22].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We provide the implementation of our approach and the reproduction package of its evaluation at https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' com/Ahmedfir/mBERTa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 6 EXPERIMENTAL RESULTS 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='1 RQ1: µBERT Additive mutations To answer this question we compare the fault detection effectiveness of test suites written to kill mutants generated by µBERT with and without additive mutations, noted re- spectively µBERT and µBERTconv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Figure 2 depicts the fault detection improvement when extending µBERT mutations by the additive ones.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In fact, µBERT fault detection increased on average by over 9% compared to the one achieved by µBERTconv, achieving 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='64% on average.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We can also see that besides outliers, the majority of bugs are found in 100% of the times.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Moreover, when examining the bugs separately, we find that µBERT finds 20 more bugs than µBERTconv (with fault detection > 0%), and 70 more when considering bugs found with fault detection percentages above 90%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This confirms that the additive patterns induce relevant mutants ensuring the detection of some bugs always or in most of the cases, as well as representing better new types of faults, which were not detectable otherwise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To check the significance of the fault detection advantage brought by the additive patterns, we performed a statistical test (Wilcoxon paired test) on the data of Figure 2a to vali- date the hypothesis ”the fault detection yielded by µBERT is greater than the one by µBERTconv ”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The very small obtained p-values of 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='92e-21 (≪ 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='05) showed that the dif- ferences are significant, indicating the low probability of this fault detection amelioration to be happening by chance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The difference size confirms also the same advantage, with ˆA12 values of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5827 (> 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5), indicating that µBERT induces test- suites with higher fault detection capability in the majority of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Next, we compare the fault detection performance of µBERT and µBERTconv when analysing the same number of mutants, and illustrate in Figure 3 their average fault BERT BERTconv tool 0 20 40 60 80 100 Fault detection % 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='64% 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='30% (a) Effectiveness: mean fault-detection per subject.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 0 20 40 60 80 100 Effort % (number of analysed mutants) 0 20 40 60 80 Fault detection % tool BERT BERTconv (b) Cost-efficiency: fault detection by the number of mutants analysed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 2: Fault-detection performance improvement when us- ing additive patterns.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Comparison between µBERT and µBERTconv, w.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='t.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' the fault-detection of test suites written to kill all generated mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' detection effectiveness and cost-efficiency in terms of anal- ysed mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The box-plots of the Subfigure 3a show that even when spending the same effort as µBERTconv, µBERT keeps a similar advantage of on average 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='05% higher fault detection, achieving a maximum of 81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='35%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' From the line- plots of the Subfigure 3b, we can see that both approaches achieve a comparable fault detection (≈ 70%) at (≤≈ 40%) of the maximum costs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' At higher costs, µBERTconv’s curve increases slowly until achieving a plateau at ≈ 60% of the effort, whereas µBERT’s curve keeps increasing to- wards higher fault detection ratios even when achieving the ≈ 100% of the fixed maximum effort.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To validate these findings we re-conducted the same statistical tests on the data of Subfigure 3a and found that µBERT outperforms significantly µBERTconv with negligible p-values of 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='15e-19 and ˆA12 values of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5711.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 7 BERT BERTconv tool 0 20 40 60 80 100 Fault detection % 81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='35% 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='30% (a) Effectiveness: mean fault-detection per subject.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 0 20 40 60 80 100 Effort % (number of analysed mutants) 0 10 20 30 40 50 60 70 80 Fault detection % tool BERT BERTconv (b) Cost-efficiency: fault detection by the number of mutants analysed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 3: Fault-detection comparison between µBERT and µBERTconv, with the same effort: where the maximum effort is limited to the minimum effort required to analyse all mutants of any of them, which is µBERTconv in most of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='2 RQ2: Fault Detection comparison with PiTest To answer this research question we reduce our dataset to the bugs covered by µBERT and the 3 considered versions of PitTest approaches: ”Pit-default” which contains the default mutation operators of PiTest, ”Pit-all” containing all PiTest operators including the default ones and ”Pit-rv-all” which contains experimental operators [7] in addition to the ”Pit- all” ones.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then, we perform the same study as in RQ1, where we compare the considered approaches’ effectiveness and cost-efficiency based on the fault detection capability of test suites written to kill their generated mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To have a fair base of comparison, we compare the approaches under the same effort in analysing mutants, which is equal to the least average effort required to kill all mutants of one of the approaches (which is the one of Pit-default in the majority of the cases).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' As we are interested in comparing the mutation testing approaches and not mutant selection strategies, we run the simulation with the same one-mutant- BERT Pit-all Pit-default Pit-rv-all tool 0 20 40 60 80 100 Fault detection % 66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='43% 60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='87% 49.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='90% 56.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='33% (a) Effectiveness: mean fault-detection per subject.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 0 20 40 60 80 100 Effort % (number of analysed mutants) 0 10 20 30 40 50 60 Fault detection % tool BERT Pit-all Pit-default Pit-rv-all (b) Cost-efficiency: fault detection by the number of mutants analysed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 4: Fault-detection comparison between µBERT and PiTest, with the same effort: where the maximum effort is limited to the minimum effort required to analyse all mutants of any of them, which is Pit-default in most of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' per-line random sampling of mutants for all techniques (see Subsection 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Figure 4b shows that with small effort (≤≈ 5%) all approaches yield comparable fault detection scores (≈ 10%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' However, the difference becomes more noticeable when spending more effort, with µBERT outperforming all ver- sions of PiTest;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' achieving on average 16.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='53% higher fault detection scores than Pit-default, 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='10% higher than Pit-rv- all and 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='56% higher than Pit-all (see Figure 4a).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To validate these results, we performed the same statis- tical tests as in RQ1, checking the hypothesis that ”µBERT yields better fault detection capabilities than the other ap- proaches”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We illustrate in the first row of Tables 2a and 2b the corresponding computed Wilcoxon paired test p-values and Vargha and Delaney ˆA12 values.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Our results show that µBERT has a significant advantage over the considered SOA approaches with p-values under 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='05.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Additionally, µBERT scores ˆA12 values above 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5 which confirms that guiding 8 TABLE 2: Paired (per subject bug) statistical tests of the average fault detection of test suites written to kill the same number of mutants generated by each approach (data of Figure 4a).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (a) Wilcoxon paired test p-values computed on every dataset subject, comparing each approach (A1) from the first column to the other approaches (A2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' p-values smaller than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='05 in- dicate that (A1) yields an average fault detection significantly higher than that of (A2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' p-values Pit-rv-all Pit-default Pit-all µBERT 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='78e-11 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='18e-12 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='32e-02 Pit-all 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='54e-22 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='87e-06 – Pit-default 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='55e-01 – – (b) Vargha and Delaney ˆA12 values computed on every dataset subject, comparing each approach (A1) from the first column to the other approaches (A2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ˆA12 values higher than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5 indicate that (A1) yields an average fault detection higher than that of (A2) in the majority of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ˆA12 Pit-rv-all Pit-default Pit-all µBERT 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='6488 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5514 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5066 Pit-all 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='7210 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='4956 – Pit-default 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5449 – – the testing by µBERT mutants instead of those generated by SOA techniques yields comparable or higher fault detection ratios, in the majority of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Indeed, the ˆA12 differ- ence between Pit-all and µBERT is small (0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5066), indicating that both approaches perform similarly or better on some studied subjects and worst on others.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We notice also from the sub-figure 4b that Pit-default achieves a plateau at around 60% of the effort while the other tools keep increasing, showing that they are able to achieve higher fault detection capabilities, at a higher cost.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This is very noticeable when we compare the sub-figures (a) and (b) of Figure 4 with the figure 2, where the average fault detection of µBERT is way lower than what it achieves in RQ1 – around 66% instead of 84%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This is a direct consequence of the fact that Pit default produces fewer mutants than the other approaches, limiting considerably the maximum effort of the mutation campaigns and thus the fault detection ratios, in the majority of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Indeed, as illustrated in Figure 5, all approaches score higher fault detection percentages when spending more effort, achieving on average ≈65% for Pit-all, ≈66% for Pit-rv-all and ≈83% for µBERT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We explain the small decrease of 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='72% in the mean fault detection achieved by µBERT in comparison with RQ1 (82,92% in RQ2 instead of 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='64% in RQ1) by the difference in the considered dataset for each RQ.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In Table 3, we illustrate the ˆA12 and p-values computed on data of the boxplots in Sub-figure 5a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The results confirm that µBERT outperforms significantly SOA mutation testing w.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='r.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='t the fault detection capability of test suites written to all kill mutants generated by each approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Next, we turned our interest to the set of particular bugs that every approach can and cannot reveal when spending the same effort.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Hence, we map each bug with its revealing tool, from the simulation results of Figure 4a and illustrate their corresponding Venn diagrams in Figure 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' BERT Pit-all Pit-default Pit-rv-all tool 0 20 40 60 80 100 Fault detection % 82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='92% 65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='49% 49.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='90% 66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='35% (a) Effectiveness: mean fault-detection per subject.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 0 20 40 60 80 100 Effort % (number of analysed mutants) 0 20 40 60 80 Fault detection % tool BERT Pit-all Pit-default Pit-rv-all (b) Cost-efficiency: fault detection by the number of mutants analysed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 5: Comparison between µBERT and PiTest, relative to the fault-detection of test suites written to kill all generated mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' From the disjoint sets in Sub-figure 6a, we notice a clear advantage in using µBERT over the considered SOA baselines, as it finds most of the bugs they find in addition to finding exclusively 47 bugs when spending the same effort.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' More precisely, µBERT finds 52, 77 and 52 more bugs than Pit-all, Pit-default and Pit-rv-all, respectively, whereas they find each 13, 10 and 13 bugs that µBERT missed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This endorses the fact that µBERT introduces mutants that represent more real bugs than SOA mutation tech- niques, and encourages the investigation of the eventual complementary between the approaches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This observation is more noticeable when considering the overlapping be- tween bugs found by each approach in at least 90% of the simulations (Sub-figure 6b).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We notice that the approaches perform comparably, with a particular distinction of Pit-all and Pit-default results which find exclusively 19 and 21 bugs with these high fault detection percentages instead of 0, as observed in Sub-figure 6a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Nevertheless, µBERT conserves the same advantage over the considered baselines in this 9 TABLE 3: Paired (per subject bug) statistical tests of the average fault detection of test suites written to kill all the mutants generated by each approach (data of Figure 5a).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (a) Wilcoxon paired test p-values computed on every dataset subject, comparing each approach (A1) from the first column to the other approaches (A2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' p-values smaller than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='05 in- dicate that (A1) yields an average fault detection significantly higher than that of (A2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' p-values Pit-rv-all Pit-default Pit-all µBERT 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='49e-13 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='14e-33 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='47e-14 Pit-all 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='71e-01 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='76e-23 – Pit-default 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='00e+00 – – (b) Vargha and Delaney ˆA12 values computed on every dataset subject, comparing each approach (A1) from the first column to the other approaches (A2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ˆA12 values higher than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5 indicate that (A1) yields an average fault detection higher than that of (A2) in the majority of the cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ˆA12 Pit-rv-all Pit-default Pit-all µBERT 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='6028 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='7123 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='6061 Pit-all 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='5077 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='6400 – Pit-default 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='3676 – – regard, finding exclusively 42 bugs more.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' It finds also 50, 63 and 69 more bugs than respectively Pit-all, Pit-default and Pit-rv-all, whereas they find each 59, 58 and 27 bugs that µBERT missed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='3 RQ3: Qualitative Analysis of µBERT Mutants To answer this research question we investigate the mutants generated by µBERT, which induced test suites able to find bugs that were not detected otherwise, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' by the considered SOA approaches (see Figure 6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Meaning that the mutants break similar tests as the target real buggy version.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' As a simple bug example (requiring only one change to fix it), we consider Lang-49 from Defects4J and we investigate mutants that have been generated by µBERT and helped in generating tests that reveal it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This bug impacts the results of the method reduce() from the class org.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='apache.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='commons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='lang.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='math.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='Fraction, which returns a new reduced fraction instance, if possible, or the same instance, otherwise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The bug is caused by a miss-implementation of a specific corner case, which con- sists of calling the method on a fraction instance that has 0 as numerator.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In Table 4, we illustrate example mutants generated by µBERT that helped in revealing this bug.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Every mutant is represented by a diff between the fixed and the mutated version by µBERT.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' As can be seen, µBERT can generate mutants that can be induced by applying conventional pattern-based mutations, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', Mutant 1 replaces a relational operator (==) with an- other (>) and Mutant 2 replaces an integer operand (0) with another one (1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In addition, it proposes more complex mutations that are unlikely achievable without any knowledge of either the AST or the context of the considered program.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For instance, it can generate Mutant 4 by changing a conditional return statement with (this) the current instance of Fraction, which matches the return type of the method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Similarly, to 47 0 1 0 1 0 3 0 3 3 23 0 1 10 354 Pit-all Pit-default Pit-rv-all BERT (a) Faults discovered at least once per 100 runs (Fault detection > 0%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 42 2 3 21 3 0 2 19 10 3 8 15 14 22 114 Pit-all Pit-default Pit-rv-all BERT (b) Faults discovered in over 90% of the runs (Fault detection≥ 90%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 6: Number of faults discovered by test-suites written to kill mutants generated by µBERT and PiTest versions when analysing the same number of mutants (same effort).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' generate Mutant 5, it replaces (this) the current instance of the class Fraction by an existent instance of the same type (ONE), making the statement returning either the object ONE or the object ZERO.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To produce more complex mutants, µBERT applies a condition seeding followed by token-masking and Code- BERT prediction, such as adding || (numerator == other.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='numerator) to the original condition of a return statement, inducing Mutant 8, or adding || !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' (numerator == Integer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='MIN_VALUE) to the original condition of an if statement, inducing Mutant 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To investigate further the impact of the code context captured by the model on the generated mutants, we have rerun µBERT on 5 subjects from our dataset, with a max- imum number of surrounding tokens equal to 10 (instead of 512).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Then, we compared manually the induced mutants with those generated by our default setup, in the same locations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' From our results, we observed a noticeable de- crease in the number of compilable predictions, indicating the general performance decrease of the model when it lacks information about the code context.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Particularly, we notice 10 TABLE 4: Example of mutants generated by µBERT that helped find the bug Lang-49 from Defects4J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 1: replacing binary operator @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 466 @@ − i f ( numerator == 0) { + i f ( numerator > 0) { Mutant 2: replacing literal implementation @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 466 @@ − i f ( numerator == 0) { + i f ( numerator == 1) { Mutant 3: adding a condition to an if statement @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 466 @@ − i f ( numerator == 0) { + i f ( ( numerator == 0) + | | !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ( numerator==Integer .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='MIN VALUE) ) { Mutant 4: replacing a condition @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 467 @@ − return equals (ZERO) ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' t h i s : ZERO;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' + return t h i s ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 5: replacing this access by another object @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 467 @@ − return equals (ZERO) ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' t h i s : ZERO;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' + return equals (ZERO) ?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ONE: ZERO;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 6: replacing method argument @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 469 @@ int gcd = greatestCommonDivisor ( − Math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' abs ( numerator ) , denominator ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' + Math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' abs ( numerator ) , 1 ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 7: replacing a variable @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 473 @@ − return Fraction .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' getFraction ( numerator / gcd , + return Fraction .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' getFraction ( numerator / 3 , denominator / gcd ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 8: adding a condition to a return statement @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 840 @@ return ( getNumerator ( ) == other .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' getNumerator ( ) − && getDenominator ( ) == other .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' getDenominator ( ) ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' + && getDenominator ( ) == other .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' getDenominator ( ) ) ) + | | ( numerator == other .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' numerator ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' that it is not able to produce program-specific mutants, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' by changing an object by another or a method call with another.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In Table 5, we illustrate some example mutants that helped find each of the studied subjects (breaking same tests as the original bug), which µBERT failed to generate when the maximum number of surrounding tokens is limited to 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 7 THREATS TO VALIDITY One external threat to validity concerns the generalisation of our findings and results in the empirical evaluation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' To reduce this threat, we used a large number of real bugs from popular open-source projects with their associated developer test-suites, provided by an established and in- dependently built benchmark (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Defects4J [29]).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Never- theless, we acknowledge that the results may be different considering projects in different domains.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Other threats may arise from our way of assessing the fault detection capability of mutation testing approaches, based on their capability of guiding the testing via a devel- oper/tester simulation in which we assume that the current test suites are complete and the not killed mutants are equivalent.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Although we acknowledge that this may not be the case in real-world scenarios, we believe that this process is sufficient to evaluate our approach, particularly considering the fact the test suites provided by Defects4J are relatively strong.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Additionally, to mitigate any com- parison threat between the considered approaches, we use consistently and similarly the same test-suites, setups and simulation assumptions in all our study.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The choice of our comparison baseline may form other threats to the validity of our findings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' While different fault- seeding approaches have been proposed recently, PiTest remains among the most mature and stable mutation test- ing tools for Java programs, thus, forming an appropriate comparison baseline to evaluate our work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Furthermore, we compared our results with those obtained by mutants from different configurations proposed by PiTest, enlarging our study to the different audiences targeted by this latter.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We acknowledge however that the results may change when considering other techniques and consider the evaluation of the effectiveness and cost-efficiency of different mutation testing techniques as out of the scope of this paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Other construct threats may arise from considering the number of mutants analysed as metric to measure the effort required to find a fault.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In addition to the fact that this metric has been widely used by the literature [9], [34], [47], we believe that it is intuitive and representative of the global manual effort of the tester in analysing the mutants, dis- carding them or writing tests to kill them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' While being the standard in the literature, we acknowledge that this measure does not account for the cost difference between mutants, attributing the same cost to all mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This is simply because we do not know the specific effort required to analyse every specific mutant or to write every specific test.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Additionally, our cost-efficiency results may be impacted by costs that are not captured with this metric, such as the execution or the developing effort of either CodeBERT, the key component of µBERT, or the set of patterns and execution enhancements over the different releases of PiTest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Nevertheless, we tried to mitigate any major threats that can be induced by the implementation of the different tools, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' we reduce the dataset subjects to those on which every approach generated at least one mutant and consider any implementation difference between the approaches as out of the current scope.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 8 RELATED WORK Since the 1970s, mutation testing has been the main focus of multiple research works [57].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Their findings have proven that artificial faults can be useful in multiple software en- gineering applications, such as testing [47], debugging [37], [48], assessing fault tolerance [42], risk analysis [16], [56] and dependability evaluation [10].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Despite this long history of research, the generation of relevant mutants remains an open question.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Most of the related research has focused on the design of fault 11 TABLE 5: Example of mutants generated by µBERT that helped in finding bugs from Defects4J and could not be generated when limiting the maximum number of surrounding tokens to 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 1 (JacksonCore-4) : replacing a method call @@ com .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' fasterxml .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' jackson .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' core .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' u t i l .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' TextBuffer : 515 @@ − unshare ( 1 ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' + expand ( 1 ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 2 (Closure-26) : replacing an object @@ com .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' google .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' j a v a s c r i p t .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' jscomp .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ProcessCommonJSModules : 89 @@ − .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' replaceAll ( Pattern .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' quote ( F i l e .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' separator ) , MODULE NAME SEPARATOR) + .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' replaceAll ( Pattern .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' quote ( filename ) , MODULE NAME SEPARATOR) Mutant 3 (Closure-35) : replacing a method call @@ com .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' google .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' j a v a s c r i p t .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' jscomp .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' TypeInference : 1092 @@ − scope = traverseChildren (n , scope ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' + scope = traverse (n , scope ) ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Mutant 4 (Lang-27) : replacing a method call @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang3 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' NumberUtils : 526 @@ − i f ( !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' i s I n f i n i t e ( ) | | ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' floatValue ( ) == 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='0 F && !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' allZeros ) ) ) { + i f ( !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' i s I n f i n i t e ( ) | | ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' round ( ) == 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='0 F && !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' allZeros ) ) ) { / / a l s o ” f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' f l o a t V a l u e ( ) ” to ” f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' s c a l e ( ) ” Mutant 5 (Math-64) : replacing an object @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Fraction : 852 @@ − for ( i nt j = k ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' j < jacobian .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' length ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ++ j ) { + for ( i nt j = k ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' j < beta .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' length ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ++ j ) { Mutant 6 (Lang-27) : replacing an object @@ org .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' apache .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' commons .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' lang3 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' math .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' NumberUtils : 526 @@ − i f ( !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' i s I n f i n i t e ( ) | | ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' floatValue ( ) == 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='0 F && !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' allZeros ) ) ) { + i f ( !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' i s I n f i n i t e ( ) | | ( f .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' round ( ) == 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='0 F && !' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' zero ) ) ) { patterns (mutation operators) which are usually defined based on the target language grammar [8], [47] then refined through empirical studies [33], [40], [44] aiming at reducing the redundancy and noise among their generated mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The continuous advances in this sense were followed by a constant emergence of pattern-based mutation testing tools and releases [17], [35], [39], among which some are becoming popular and widely adopted by researchers and practitioners, such as PiTest [17], from which we consider three configurations as our comparison baseline.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Recent research has focused their interest on improving the representativeness of artificial faults aiming at reducing the mutation space to real-like faults.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For instance, instead of basing the mutation operators’ design on the programming language grammar, Brown et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [12] proposed inferring them from real bug fixes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Similarly, Tufano et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [54] pro- posed a neural machine translation technique that learns how to inject faults from real bug fixes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Along the same line, Patra et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [50] proposed a semantic-aware learning approach, that learns and then adapts fault patterns to the project of interest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Their results are promising, however, the fact that these techniques depend on the availability of numerous, diverse, comprehensive and untangled fix commits [27] of not coupled faults [43], which is often hard to fulfil in practice, may hinder their performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Acknowl- edging for the injection location [13], [42], Khanfir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [32] combined the usage of information retrieved from bug reports with inverted automated-program-repair patterns to replicate real faults fixable by the original fix-patterns.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Their results showed that they can generate faults that mimic real ones, however, their approach remains dependent and lim- ited to the presence of good bug reports.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Overall, designing the mutation operators based on the known faults space yields more diverse mutants that represent more fault types.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' However, these extended operator sets tend to increase the number of generated mutants and consequently the general cost of the mutation campaign i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' the fault patterns pro- posed by Brown et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' and Khanfir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' counted also most of the conventional mutators in addition to new ones.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Unlike these techniques, µBERT leverages pre-trained models to introduce mutants based on code knowledge instead of the faults one.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' As code is more available than faults, it offers a more flexible and complete knowledge base than faults, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' it perms to overcome the limitations and efforts required 1) to collect clean bug-fixing commits, 2) to capture the faulty behaviour and 3) design fault patterns, be it manually or via machine learning techniques.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Aiming at reducing the number of generated mutants, researchers have proposed different strategies to generate relevant mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' For instance, studies that show that mu- tant strength resides in not only its inducing pattern but also where it is injected [13], [42], motivated the importance of selecting relevant locations to mutate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In this regard, Sun et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [53] suggest mutating multiple places within diverse program execution paths.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Gong et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [26] also propose the mutation in diverse locations of the program extracted from graph analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Similarly, Mirshokraie et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [41] compute complexity metrics from program executions to extract loca- 12 tions with good observability to mutate.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Other approaches restrict the fault injection on specific locations of the pro- gram, such as the code impacted by the last commits [38], [58] for better usability in continuous integration, or target- ing locations related to a given bug-report [32] to target a specific feature or behaviour, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' More recent advances have resulted in powerful techniques for cost-effectively selecting mutants, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=', by avoiding the analysis of redundant mutants (basically, equivalent and subsumed ones) [24], [25], [28].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In particular, the work of Garg et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [24] utilises the knowledge of mutants’ surrounding context, embedded into the vector space, to predict whether a mutant is likely subsuming or not.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In this work, we do not target any specific code part or any narrow use case, but instead, perform fault injection in a brute-force way similarly to mutation testing, by iterating every program statement and masking every involved token.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Multiple studies have been focused on the relationship between artificial and real faults [47].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The results of the stud- ies conducted by Ojdanic et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [45], Papadakis et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [49], Just et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [30] and Andrews et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [9] showed that there is a correlation between tests broken by a bug and tests killing mutants.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Meaning that artificial faults can be used as alternatives to real faults in controlled studies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Moreover, the findings of Chekam et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [14], Frankl et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [23] and Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' [36] show that guiding testing by mutants leads to significantly higher fault revelation capability than the ones of other test adequacy criteria.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Based on these findings, we assess our approach based on the relation between the injected and real faults, in terms of breaking tests.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' More precisely, we conduct a fault detection effectiveness and cost-efficiency study to evaluate our approach’s mutants in guiding testing and compare it to state-of-the-art techniques.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Furthermore, we discuss the diversity and readability of µBERT mutants through real examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The closest related work is a preliminary implementation of µBERT that was recently presented in the 2022 mutation workshop [18].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This implementation, denoted as µBERTconv in our evaluation, includes the conventional mutations (to mask and replace tokens by the model predictiosn), but it does not include the condition-seeding additive mutations that provide major benefits for fault detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Moreover, µBERTconv was evaluated only on 40 bugs from Defects4J, and compared only to an early version of PiTest (similar to Pit-rv-all).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In this work, we perform an extensive exper- imental evaluation including 689 bugs from Defects4J and compare µBERT effectiveness with three different configura- tions from PiTest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Moreover, we show that µBERT finds on average more bugs than µBERTconv without requiring more effort.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 9 CONCLUSION We presented µBERT;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' a pre-trained language model based fault injection approach.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' µBERT provides researchers and practitioners with easy-to-understand “natural” mutantsto help them in writing tests of higher fault revelation capabil- ities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' Unlike state-of-the-art approaches, it does neither re- quire nor depend on any kind of faults knowledge or language grammar but instead on the actual code definition and distribution, as written by developers in numerous projects.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This facilitates its developing, maintainability, inte- gration and extension to different programming languages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In fact, it reduces the overhead of learning how to mutate, be it via creating and selecting patterns or collecting good bug-fixes and learning from their patches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' In a nutshell, µBERT takes as input a given program and replaces different pieces of its code base with predictions made by a pretrained generative language model, produc- ing multiple likely-to-occur mutations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' The approach targets diverse business code locations and injects either simple one-token replacement mutants or more complex ones by extending the control-flow conditions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This provides proba- ble developer-like faults impacting different functionalities of the program with higher relevance and lower cost to developers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' This is further endorsed by our results where µBERT induces high fault detection test suites at low effort, outperforming state-of-the-art techniques (PiTest), in this regard.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' We have made our implementation and results avail- able [5] to enable reproducibility and support future re- search.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' ACKNOWLEDGMENT This work was supported by the Luxembourg National Research Fund (FNR) projects C20/IS/14761415/TestFlakes and TestFast, ref.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' 12630949.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' REFERENCES [1] Amazon codewhisperer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content=' https://aws.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='amazon.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/-NE1T4oBgHgl3EQf8QVm/content/2301.03543v1.pdf'} +page_content='com/ 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