Toward Trace-Guided Automated Program Repair

Abstract

Fixing bugs is a time-consuming task. Researchers and developers hence aim to automate program repair (APR) as much as possible. Recent advances in Large Language Models (LLMs) made automated fixing of bugs widely accessible to practitioners. However, while LLMs are in principle capable of generating code and fixing programs, their ability to locate and therefore effectively fix bugs is limited by their context length. To guide them towards the parts of the program that require fixing, LLMs could be prompted with information from dynamic analysis techniques. We expect that the most efficient way to help LLMs debug code is by providing the LLM with information to interactively find the bug location. To do so, we propose two steps: Tailoring the prompt with trace information and giving the LLM a tool call to get further trace information. This trace information needs to be preprocessed in a way that it can be efficiently used by the LLM and makes it possible to understand the program behavior. In this paper, we describe our concept of an APR approach that is guided by tracing data and which is therefore capable of fixing bugs in huge codebases. As a proof of concept, we show that in the bug dataset defects4j, location information achieves an absolute increase of up to 22.4 % in fixed bugs with Gemma-4-31B compared to a baseline without location information. Based on our concept and our proof of concept, we outline the research agenda that is necessary to enable trace-guided APR.