The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs
Authors: name: Muntasir Adnan, name: Carlos C. N. Kuhn

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.