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.
6825c9c7c6f06669c8fa6813
Revanth Gangi Reddy
6825c9c7c6f06669c8fa6814
Tarun Suresh
claimed_verified
2025-06-05T07:29:12.767Z
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/avatars/3c0972f0d59e51ebb5c218ee736d4458.svg
Tarun Suresh
user
tarsur909
6825c9c7c6f06669c8fa6815
JaeHyeok Doo
6825c9c7c6f06669c8fa6816
Ye Liu
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Xuan Phi Nguyen
6825c9c7c6f06669c8fa6818
Yingbo Zhou
6825c9c7c6f06669c8fa6819
Semih Yavuz
6825c9c7c6f06669c8fa681a
Caiming Xiong
6825c9c7c6f06669c8fa681b
Heng Ji
6825c9c7c6f06669c8fa681c
Shafiq Joty
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.
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.
retrieve-and-rerank SweRank SweLoc dataset GitHub repositories issue localization SWE-Bench-Lite LocBench retriever reranker