Tracing Errors, Constructing Fixes: Repository-Level Memory Error Repair via Typestate-Guided Context Retrieval

• URL: http://arxiv.org/abs/2506.18394v1
• Date: Mon, 23 Jun 2025 08:30:00 GMT
• Title: Tracing Errors, Constructing Fixes: Repository-Level Memory Error Repair via Typestate-Guided Context Retrieval
• Authors: Xiao Cheng, Zhihao Guo, Huan Huo, Yulei Sui,
• Abstract summary: This paper introduces LTFix, a novel approach that harnesses the potential of Large Language Models (LLMs) for automated memory error repair.<n>Our approach utilizes a finite typestate automaton to guide the tracking of error-propagation paths and context trace, capturing both spatial (memory states) and temporal (execution history) dimensions of error behavior.
• Score: 9.737526306478598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory-related errors in C programming continue to pose significant challenges in software development, primarily due to the complexities of manual memory management inherent in the language. These errors frequently serve as vectors for severe vulnerabilities, while their repair requires extensive knowledge of program logic and C's memory model. Automated Program Repair (APR) has emerged as a critical research area to address these challenges. Traditional APR approaches rely on expert-designed strategies and predefined templates, which are labor-intensive and constrained by the effectiveness of manual specifications. Deep learning techniques offer a promising alternative by automatically extracting repair patterns, but they require substantial training datasets and often lack interpretability. This paper introduces LTFix, a novel approach that harnesses the potential of Large Language Models (LLMs) for automated memory error repair, especially for complex repository-level errors that span multiple functions and files. We address two fundamental challenges in LLM-based memory error repair: a limited understanding of interprocedural memory management patterns and context window limitations for repository-wide analysis. Our approach utilizes a finite typestate automaton to guide the tracking of error-propagation paths and context trace, capturing both spatial (memory states) and temporal (execution history) dimensions of error behavior. This typestate-guided context retrieval strategy provides the LLM with concise yet semantically rich information relevant to erroneous memory management, effectively addressing the token limitation of LLMs.

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