The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

2025-06-23

AI Summary

AI-based code debugging suffers from significant performance decay: most models exhibit a 60–80% decline in debugging capability after just 2–3 iterative rounds, severely undermining long-term debugging effectiveness. Method: This paper introduces the Debugging Decay Index (DDI), the first mathematical framework to quantitatively model the exponential decay of debugging performance. Leveraging DDI, we design a strategic restart mechanism that dynamically balances exploitation and exploration, augmented by intervention-point prediction to optimize debugging trajectories. Results: Experiments demonstrate that our approach suppresses 60–80% of performance decay within the first 2–3 rounds, substantially enhancing the robustness and long-horizon effectiveness of large language models in sequential debugging tasks. Core contributions include (i) uncovering the fundamental debugging decay phenomenon, (ii) establishing DDI—the first quantifiable diagnostic metric for debugging degradation, and (iii) proposing a proactive intervention strategy grounded in decay modeling.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Sequential Decision Making
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Quantifies AI debugging decay with Debugging Decay Index
• Predicts intervention points to maintain debugging effectiveness
• Proposes fresh start strategy to optimize code generation

Innovation

• Introduces Debugging Decay Index (DDI) framework
• Proposes strategic fresh start approach
• Quantifies and optimizes debugging effectiveness