h4/solfunmeme-dioxus-reports

Rust AST Emoji Dataset

Dataset Summary

This dataset contains Rust codebase AST (Abstract Syntax Tree) analysis with emoji mapping for code understanding and visualization. The dataset provides a unique perspective on code structure by mapping AST node types and extracted words to emojis, enabling creative code analysis and visualization.

Supported Tasks and Leaderboards

• Code Understanding: Analyze code structure through emoji patterns
• Code Classification: Identify code domains (Crypto, Web, i18n, etc.) through emoji signatures
• Code Visualization: Create emoji-based code summaries and visualizations
• Pattern Recognition: Discover common coding patterns through emoji frequency analysis

Languages

The dataset contains Rust source code with English comments and identifiers.

Dataset Structure

Data Instances

Each instance contains:

• file_path: Path to the original Rust source file
• timestamp: Unix timestamp of analysis
• ast: Full AST representation in JSON format
• summary: Analysis summary including:
  • top_level_nodes: Number of top-level AST nodes
  • total_nodes: Total number of AST nodes
  • type_counts: Count of each AST node type
  • string_literals: Extracted string literals
  • word_counts: Word frequency analysis
  • word_emoji_counts: Emoji mapping for words
  • emoji_counts_in_strings: Emojis found in string literals

Data Fields

• file_path (string): Path to the original Rust source file
• timestamp (int64): Unix timestamp of analysis
• ast (string): Full AST representation in JSON
• summary (map): Analysis summary with nested fields:
  • top_level_nodes (int64): Number of top-level AST nodes
  • total_nodes (int64): Total number of AST nodes
  • type_counts (map): Count of each AST node type
  • string_literals (sequence): Extracted string literals
  • word_counts (map): Word frequency analysis
  • word_emoji_counts (map): Emoji mapping for words
  • emoji_counts_in_strings (map): Emojis found in string literals

Data Splits

• train: All analyzed Rust files

Dataset Creation

Source Data

Initial Data Collection and Normalization

The dataset was created by analyzing Rust source files from the solfunmeme-dioxus project, which includes:

• Core application code
• Vendor dependencies
• Generated code
• Test files

Who are the source language producers?

The source code was written by developers working on the solfunmeme-dioxus project, including contributions from the open-source community.

Annotations

Annotation process

The annotation process involved:

1. AST Parsing: Using syn crate to parse Rust source files into ASTs
2. Emoji Mapping: Mapping AST node types and extracted words to emojis based on semantic categories
3. Analysis: Extracting string literals, word frequencies, and emoji patterns
4. Chunking: Splitting large datasets into manageable chunks (1MB each)

Who are the annotators?

The annotations were generated automatically using a custom Rust script that implements emoji mapping based on predefined categories.

Personal and Sensitive Information

The dataset contains only code analysis data and does not include personal or sensitive information. All file paths are relative to the project structure.

Additional Information

Dataset Curators

The dataset was curated as part of the solfunmeme-dioxus project development process.

Licensing Information

This dataset is licensed under AGPL-3.0, the same license as the source codebase.

Citation Information

@dataset{rust_ast_emoji,
  title={Rust AST Emoji Dataset},
  author={solfunmeme-dioxus contributors},
  year={2024},
  url={https://github.com/meta-introspector/solfunmeme-dioxus}
}

Contributions

Contributions to improve the dataset, emoji mappings, or analysis methods are welcome through the project's GitHub repository.

Usage Examples

Basic Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("h4/solfunmeme-dioxus-reports")

# Access a sample
sample = dataset["train"][0]
print(f"File: {sample['file_path']}")
print(f"Top-level nodes: {sample['summary']['top_level_nodes']}")
print(f"Total nodes: {sample['summary']['total_nodes']}")

Emoji Analysis

# Analyze emoji patterns
emoji_counts = sample['summary']['word_emoji_counts']
for emoji, count in emoji_counts.items():
    print(f"{emoji}: {count}")

Code Domain Detection

The dataset enables detection of code domains through emoji patterns:

• 🌵 (Agave): Solana/blockchain code
• 🎨 (CSS): Frontend/styling code
• 🔒 (Crypto): Security/cryptography code
• 🌐 (i18n): Internationalization code

Technical Details

Chunking Strategy

The dataset is split into chunks of maximum 1MB each to comply with Hugging Face and GitHub file size limits. Each chunk contains multiple code analysis examples.

Emoji Mapping Categories

The emoji mapping covers several categories:

• Rust Core: Basic Rust language constructs (🦀⚙️, 🏛️🧱, etc.)
• Web/CSS: Frontend and styling concepts (📏, 🧭, etc.)
• Crypto/Security: Cryptography and security (🔒, 🔑, etc.)
• Project-Specific: Domain-specific terms (🌵, 🌞, etc.)
• Internationalization: i18n and localization (🌐, 🌍, etc.)
• Testing/Benchmarking: Testing and performance (⏱️, 🏋️, etc.)

Performance Considerations

The dataset is optimized for:

• Memory efficiency: Compact JSON serialization
• Accessibility: Small chunk sizes for easy loading
• Scalability: Organized directory structure for large datasets