SweRank: Software Issue Localization with Code Ranking
Authors: name: Revanth Gangi Reddy, name: Tarun Suresh, name: JaeHyeok Doo, name: Ye Liu, name: Xuan Phi Nguyen, name: Yingbo Zhou, name: Semih Yavuz, name: Caiming Xiong, name: Heng Ji, name: Shafiq Joty

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.