SweRank: Software Issue Localization with Code Ranking

Software issue localization, the task of identifying the precise code locations (files, classes, or functions) relevant to a natural language issue description (e.g., bug report, feature request), is a critical yet time-consuming aspect of software development. While recent LLM-based agentic approaches demonstrate promise, they often incur significant latency and cost due to complex multi-step reasoning and relying on closed-source LLMs. Alternatively, traditional code ranking models, typically optimized for query-to-code or code-to-code retrieval, struggle with the verbose and failure-descriptive nature of issue localization queries. To bridge this gap, we introduce SweRank, an efficient and effective retrieve-and-rerank framework for software issue localization. To facilitate training, we construct SweLoc, a large-scale dataset curated from public GitHub repositories, featuring real-world issue descriptions paired with corresponding code modifications. Empirical results on SWE-Bench-Lite and LocBench show that SweRank achieves state-of-the-art performance, outperforming both prior ranking models and costly agent-based systems using closed-source LLMs like Claude-3.5. Further, we demonstrate SweLoc's utility in enhancing various existing retriever and reranker models for issue localization, establishing the dataset as a valuable resource for the community.

@article{reddy2025_2505.07849,
  title={ SweRank: Software Issue Localization with Code Ranking },
  author={ Revanth Gangi Reddy and Tarun Suresh and JaeHyeok Doo and Ye Liu and Xuan Phi Nguyen and Yingbo Zhou and Semih Yavuz and Caiming Xiong and Heng Ji and Shafiq Joty },
  journal={arXiv preprint arXiv:2505.07849},
  year={ 2025 }
}