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Measuring and mitigating debugging effectiveness decay in code language models.

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AI debugging effectiveness decays rapidly, losing most capability within 3 attempts. A new Debugging Decay Index (DDI) quantifies this decay and guides interventions to improve AI code generation.

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Area of Science:

  • Artificial Intelligence
  • Software Engineering
  • Computer Science

Background:

  • Iterative debugging is crucial for AI code generation systems.
  • Current AI models exhibit significant performance degradation in debugging over successive attempts.

Purpose of the Study:

  • To quantify the decay in AI debugging effectiveness.
  • To introduce a framework for predicting debugging ineffectiveness.
  • To propose a strategy for improving AI debugging.

Main Methods:

  • Developed the Debugging Decay Index (DDI) as a mathematical framework.
  • Analyzed the exponential decay pattern of AI debugging capability.
  • Implemented a strategic fresh start approach to AI debugging.

Main Results:

  • AI debugging effectiveness follows an exponential decay, losing 60-80% capability within 2-3 attempts.
  • The DDI framework accurately predicts when AI debugging becomes ineffective.
  • Strategic interventions can significantly restore AI debugging effectiveness.

Conclusions:

  • Current AI self-debugging has fundamental limitations.
  • The DDI provides a systematic metric for evaluating LLM-based code generation.
  • A strategic fresh start approach enhances AI debugging performance.