ICML2026
SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
Jingxuan Xu, Ken Deng, Weihao Li, Songwei Yu, Haoyang Huang, Xinping Lei, Yifan Yao, Huaixi Tang, Zhiyi Lai, Kepeng Lei, Zizheng Zhan, Yanan Wu, Chenchen Zhang, Wenqiang Zhu, wen xiang, Zongxian Feng, Han Li, Junqi Xiong, Dailin Li, GAO Zuchen, Kun Wu, Yuanxing Zhang, Wuxuan Gong, Ziyuan Gao, Guanxiang Wang, Yirong Xue, Mengfei Xie, Xiaojiang Zhang, Jinghui Wang, Wenhao Zhuang, Zheng Lin, Huiming Wang, Zhaoxiang Zhang, Yuqun Zhang, Haotian Zhang, Ming Sun, chenbin, Jiaheng Liu
9 citations
Abstract
Evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer workflows. Existing benchmarks often focus on algorithmic problems or Pythoncentric bug fixing, leaving critical dimensions of software engineering underexplored. To address these gaps, we introduce SWE-Compass 1 , a comprehensive benchmark that unifies heterogeneous code-related evaluations into a structured and productionaligned framework. SWE-Compass spans 8 task types, 8 programming scenarios, and 10 programming languages, with 2000 high-quality instances curated from authentic GitHub pull requests and refined through systematic filtering and validation. We benchmark ten state-of-the-art LLMs under two agentic frameworks, SWE-Agent and Claude Code, revealing a clear hierarchy of difficulty across task types, languages, and scenarios. Moreover, by aligning evaluation with real-world developer practices, we hope SWE-Compass can provide a rigorous and reproducible foundation for diagnosing and advancing agentic coding capabilities in large language models.