DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information

February 09, 2026

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Title: DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information
ArXiv ID: 2512.24635
Date: 2025-12-31
Authors: Zhili Huang, Ling Xu, Chao Liu, Weifeng Sun, Xu Zhang, Yan Lei, Meng Yan, Hongyu Zhang

📝 Abstract

Automated Program Repair (APR) aims to generate correct patches for buggy programs automatically. Recent approaches leveraging large language models (LLMs) have shown promise but face notable limitations. Most existing methods rely solely on static analysis, ignoring the runtime behaviors of programs. Although some studies attempt to incorporate dynamic signals, most methods either restrict it to the training or fine-tuning phase, or inject it only once into the repair prompt, without leveraging it iteratively throughout the repair process. This limited usage fails to fully capture program execution. In addition, current iterative repair frameworks typically rely on coarse-grained feedback, such as pass/fail results or exception types, without effectively utilizing fine-grained execution-level information. This makes it difficult for models to simulate the stepwise debugging process of human developers, resulting in limited effectiveness in multi-step reasoning and complex bug repair. To address these challenges, we propose DynaFix, an execution-level dynamic information-driven APR method that iteratively leverages runtime information to refine the repair process. In each repai...

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DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information

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