title: SweRank: Software Issue Localization with Code Ranking
summary: 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.
paper_authors._id: 6825c9c7c6f06669c8fa6813
paper_authors.name: Revanth Gangi Reddy
paper_authors._id: 6825c9c7c6f06669c8fa6814
paper_authors.name: Tarun Suresh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:29:12.767Z
paper_authors._id: 65e7bb35e5e78134ab049942
paper_authors.avatarUrl: /avatars/3c0972f0d59e51ebb5c218ee736d4458.svg
paper_authors.fullname: Tarun Suresh
paper_authors.type: user
paper_authors.user: tarsur909
paper_authors._id: 6825c9c7c6f06669c8fa6815
paper_authors.name: JaeHyeok Doo
paper_authors._id: 6825c9c7c6f06669c8fa6816
paper_authors.name: Ye Liu
paper_authors._id: 6825c9c7c6f06669c8fa6817
paper_authors.name: Xuan Phi Nguyen
paper_authors._id: 6825c9c7c6f06669c8fa6818
paper_authors.name: Yingbo Zhou
paper_authors._id: 6825c9c7c6f06669c8fa6819
paper_authors.name: Semih Yavuz
paper_authors._id: 6825c9c7c6f06669c8fa681a
paper_authors.name: Caiming Xiong
paper_authors._id: 6825c9c7c6f06669c8fa681b
paper_authors.name: Heng Ji
paper_authors._id: 6825c9c7c6f06669c8fa681c
paper_authors.name: Shafiq Joty
paper_title: SweRank: Software Issue Localization with Code Ranking
paper_summary: 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.
paper_ai_summary: SweRank, an efficient retrieve-and-rerank framework using the SweLoc dataset, achieves state-of-the-art performance in software issue localization, outperforming both traditional models and agent-based systems.
paper_ai_keywords: retrieve-and-rerank SweRank SweLoc dataset GitHub repositories issue localization SWE-Bench-Lite LocBench retriever reranker