SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908643559997440 |
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| author | Ma, Jeffrey Jian Hashemi, Milad Yazdanbakhsh, Amir Swersky, Kevin Press, Ofir Li, Enhui Reddi, Vijay Janapa Ranganathan, Parthasarathy |
| author_facet | Ma, Jeffrey Jian Hashemi, Milad Yazdanbakhsh, Amir Swersky, Kevin Press, Ofir Li, Enhui Reddi, Vijay Janapa Ranganathan, Parthasarathy |
| contents | Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness. However, most benchmarks emphasize what to fix rather than how to fix code. We introduce SWE-fficiency, a benchmark for evaluating repository-level performance optimization on real workloads. Our suite contains 498 tasks across nine widely used data-science, machine-learning, and HPC repositories (e.g., numpy, pandas, scipy): given a complete codebase and a slow workload, an agent must investigate code semantics, localize bottlenecks and relevant tests, and produce a patch that matches or exceeds expert speedup while passing the same unit tests. To enable this how-to-fix evaluation, our automated pipeline scrapes GitHub pull requests for performance-improving edits, combining keyword filtering, static analysis, coverage tooling, and execution validation to both confirm expert speedup baselines and identify relevant repository unit tests. Empirical evaluation of state-of-the-art agents reveals significant underperformance. On average, agents achieve less than 0.15x the expert speedup: agents struggle in localizing optimization opportunities, reasoning about execution across functions, and maintaining correctness in proposed edits. We release the benchmark and accompanying data pipeline to facilitate research on automated performance engineering and long-horizon software reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads? Ma, Jeffrey Jian Hashemi, Milad Yazdanbakhsh, Amir Swersky, Kevin Press, Ofir Li, Enhui Reddi, Vijay Janapa Ranganathan, Parthasarathy Software Engineering Artificial Intelligence Performance Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness. However, most benchmarks emphasize what to fix rather than how to fix code. We introduce SWE-fficiency, a benchmark for evaluating repository-level performance optimization on real workloads. Our suite contains 498 tasks across nine widely used data-science, machine-learning, and HPC repositories (e.g., numpy, pandas, scipy): given a complete codebase and a slow workload, an agent must investigate code semantics, localize bottlenecks and relevant tests, and produce a patch that matches or exceeds expert speedup while passing the same unit tests. To enable this how-to-fix evaluation, our automated pipeline scrapes GitHub pull requests for performance-improving edits, combining keyword filtering, static analysis, coverage tooling, and execution validation to both confirm expert speedup baselines and identify relevant repository unit tests. Empirical evaluation of state-of-the-art agents reveals significant underperformance. On average, agents achieve less than 0.15x the expert speedup: agents struggle in localizing optimization opportunities, reasoning about execution across functions, and maintaining correctness in proposed edits. We release the benchmark and accompanying data pipeline to facilitate research on automated performance engineering and long-horizon software reasoning. |
| title | SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads? |
| topic | Software Engineering Artificial Intelligence Performance |
| url | https://arxiv.org/abs/2511.06090 |