SWE-Perf: Can Language Models Optimize Code Performance on Real-World
  Repositories?
Authors: name: Xinyi He, name: Qian Liu, name: Mingzhe Du, name: Lin Yan, name: Zhijie Fan, name: Yiming Huang, name: Zejian Yuan, name: Zejun Ma

Code performance optimization is paramount in real-world software engineering
and critical for production-level systems. While Large Language Models (LLMs)
have demonstrated impressive capabilities in code generation and bug fixing,
their proficiency in enhancing code performance at the repository level remains
largely unexplored. To address this gap, we introduce SWE-Perf, the first
benchmark specifically designed to systematically evaluate LLMs on code
performance optimization tasks within authentic repository contexts. SWE-Perf
comprises 140 carefully curated instances, each derived from
performance-improving pull requests from popular GitHub repositories. Each
benchmark instance includes the relevant codebase, target functions,
performance-related tests, expert-authored patches, and executable
environments. Through a comprehensive evaluation of representative methods that
span file-level and repo-level approaches (e.g., Agentless and OpenHands), we
reveal a substantial capability gap between existing LLMs and expert-level
optimization performance, highlighting critical research opportunities in this
emerging field.