iCoRe: An Iterative Correlation-Aware Retriever for Bug Reproduction Test Generation

Automatically generating bug reproduction tests (BRT) from issue descriptions is crucial for facilitating software maintenance. Large Language Model (LLM)-based approaches have shown great potential for this task. Their effectiveness heavily relies on retrieving high-quality context from the codebase. The retrieval phase of existing approaches relies on either traditional methods like BM25 or modern LLM-driven strategies. The LLM-based retrieval strategies typically involve equipping an LLM with tools to autonomously explore the code repository or having it select the most relevant files and code snippets from a provided list as context. However, these retrieval methods suffer from three key limitations: (1) They often employ a unified strategy for retrieving both source code and test cases, overlooking their distinct retrieval requirements. (2) They focus solely on semantic similarity, ignoring function call relationships that reflect behavioral relevance, which often leads to the retrieval of irrelevant context. (3) The retrieval lacks a feedback loop from the generation phase, preventing it from refining the context based on execution results. These limitations collectively result in low-quality context, thereby hindering the accuracy of bug reproduction. To address these challenges, we propose iCoRe, an iterative, correlation-aware context retrieval approach. iCoRe is explicitly designed to be aware of three key correlations: 1) the correlation between source code and test cases, which requires differentiated retrieval, 2) the correlation between textual semantics and function call structures for accurate relevance assessment, and 3) the correlation between the retrieval and generation phases, which enables iterative feedback and refinement. To evaluate iCoRe, we integrate it with an LLM-based BRT generator and conduct a comprehensive evaluation on the SWT-bench Lite and TDD-bench Verified benchmarks. Experimental results show that our method achieves a Fail-to-Pass rate of 42.0% and 52.8% respectively, representing significant 19.7%--31.7% relative improvements over existing retrieval methods.