The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

I'm excited to share our latest research identifying a fundamental limitation in how AI models approach iterative debugging: "The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs"

We developed the Debugging Decay Index (DDI), a mathematical framework that quantifies when debugging becomes ineffective and predicts optimal intervention points. Our strategic "fresh start" approach clears conversation history at calculated thresholds rather than continuing failed debugging attempts.
Results: Testing across 18 state-of-the-art models showed consistent improvements with strategic restarts. Notable examples include Llama3.1 improving from 72.6% to 82.8% accuracy and DeepSeek-Coder-V2 from 84.1% to 92.1%, with no additional computational cost.
Implications: This challenges the assumption that more debugging iterations necessarily improve outcomes. The exponential decay pattern suggests models become trapped in failing solution approaches rather than exploring alternatives.
The DDI framework provides both a new evaluation metric for iterative code generation and a practical strategy for optimising debugging workflows. The mathematical formulation appears robust across different model architectures.