除錯衰減指數：重新思考程式碼大型語言模型的除錯策略

摘要

AI除錯的有效性遵循可預測的指數衰減模式；大多數模型在僅2-3次嘗試後便喪失60-80%的除錯能力，儘管迭代除錯對於實際的程式碼生成系統至關重要。我們引入了除錯衰減指數（Debugging Decay Index, DDI），這是一個數學框架，用於量化除錯何時變得無效並預測介入點。我們的策略性重新開始方法在除錯過程的關鍵點從利用轉向探索，證明了適時的介入能夠挽救除錯的有效性。DDI揭示了當前AI除錯中的一個根本性限制，並提供了首個用於優化迭代程式碼生成策略的量化框架。

English

The effectiveness of AI debugging follows a predictable exponential decay pattern; most models lose 60-80% of their debugging capability within just 2-3 attempts, despite iterative debugging being a critical capability for practical code generation systems. We introduce the Debugging Decay Index (DDI), a mathematical framework that quantifies when debugging becomes ineffective and predicts intervention points. Our strategic fresh start approach shifts from exploitation to exploration at strategic points in the debugging process, demonstrating that well-timed interventions can rescue the effectiveness of debugging. DDI reveals a fundamental limitation in current AI debugging and provides the first quantitative framework for optimising iterative code generation strategies.

除錯衰減指數：重新思考程式碼大型語言模型的除錯策略

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

摘要

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