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Bug fixing holds significant importance in software development andmaintenance. Recent research has made notable progress in exploring thepotential of large language models (LLMs) for automatic bug fixing. However,existing studies often overlook the collaborative nature of bug resolution,treating it as a single-stage process. To overcome this limitation, weintroduce a novel stage-wise framework named STEAM in this paper. The objectiveof STEAM is to simulate the interactive behavior of multiple programmersinvolved in various stages across the bug's life cycle. Taking inspiration frombug management practices, we decompose the bug fixing task into four distinctstages: bug reporting, bug diagnosis, patch generation, and patch verification.These stages are performed interactively by LLMs, aiming to imitate thecollaborative abilities of programmers during the resolution of software bugs.By harnessing the collective contribution, STEAM effectively enhances thebug-fixing capabilities of LLMs. We implement STEAM by employing the powerfuldialogue-based LLM -- ChatGPT. Our evaluation on the widely adopted bug-fixingbenchmark demonstrates that STEAM has achieved a new state-of-the-art level ofbug-fixing performance.

STEAM: Simulating the InTeractive BEhavior of ProgrAMmers for Automatic Bug Fixing