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Identifying, localizing, and resolving bugs in software engineering is challenging and costly. Approaches to resolve software bugs range from Large Language Model (LLM) code analysis and repair, and automated code repair technology that aims to alleviate the technical burden of difficult to solve bugs. We propose RAGFix, which enhances LLM’s capabilities for bug localization and code repair using Retrieval Augmented Generation (RAG) based on dynamically collected Stack Overflow posts. These posts are searchable via a Question and Answer Knowledge Graph (KGQA). We evaluate our method on the HumanEvalFix benchmark for Python using relevant closed and open-source models. Our approach facilitates error resolution in Python coding problems by creating a searchable, embedded knowledge graph representation of bug and solution information from Stack Overflow, interlinking bugs, and solutions through semi-supervised graph construction methods. We use cosine similarity on embeddings based on LLM-synthesized summaries and algorithmic features describing the coding problem and potential solution to find relevant results that improve LLM in-context performance. Our results indicate that our system enhances small open-source models’ ability to effectively repair code, particularly where these models have less parametric knowledge about relevant coding problems and can leverage nonparametric knowledge to provide accurate, actionable fixes. 

Identifying, localizing, and resolving bugs in software engineering is challenging and costly. Approaches to resolve software bugs range from Large Language Model (LLM) code analysis and repair, and automated code repair technology that aims to alleviate the technical burden of difficult to solve bugs. We propose RAGFix, which enhances LLM’s capabilities for bug localization and code repair using Retrieval Augmented Generation (RAG) based on dynamically collected Stack Overflow posts. These posts are searchable via a Question and Answer Knowledge Graph (KGQA). We evaluate our method on the HumanEvalFix benchmark for Python using relevant closed and open-source models. Our approach facilitates error resolution in Python coding problems by creating a searchable, embedded knowledge graph representation of bug and solution information from Stack Overflow, interlinking bugs, and solutions through semi-supervised graph construction methods. We use cosine similarity on embeddings based on LLM-synthesized summaries and algorithmic features describing the coding problem and potential solution to find relevant results that improve LLM in-context performance. Our results indicate that our system enhances small open-source models’ ability to effectively repair code, particularly where these models have less parametric knowledge about relevant coding problems and can leverage nonparametric knowledge to provide accurate, actionable fixes.