Findings show that functional-only evaluation overestimates agents'ability to satisfy the full requirements of repository-level repair tasks, and introduces SWE-Gate, a repository-level benchmark for software engineering agents that explicitly evaluates review constraint compliance alongside functional correctness.
Abstract
Repository-level software engineering benchmarks have significantly advanced the evaluation of coding agents, but existing benchmarks primarily measure whether generated patches pass functional tests and overlook review-derived acceptance constraints (review constraints) that often influence whether a patch is acceptable in real-world software development. We introduce SWE-Gate, a repository-level benchmark for software engineering agents that explicitly evaluates review constraint compliance alongside functional correctness. SWE-Gate derives review constraints from real pull request review comments and synthesizes repository-level repair instances around these constraints. Each instance provides separate functional and constraint tests, together with non-compliant and gold patches, enabling explicit separation between issue resolution capability and review constraint compliance. We construct SWE-Gate with 303 repository-level repair instances spanning 75 open-source Python repositories across diverse software domains. Experiments with four LLM backends spanning different capability levels under a common coding-agent scaffold reveal a substantial gap between functional success and success under the complete repair specification: among 644 repairs that pass the functional tests, 221 fail to satisfy the provided review constraints. These findings show that functional-only evaluation overestimates agents'ability to satisfy the full requirements of repository-level repair tasks. The replication package including code, data, and experimental results is available at https://github.com/DeepSoftwareAnalytics/SWE-Gate.
SWE-RPG is introduced, a repository-level benchmark that combines executable patch evaluation with validated ground-truth references (GTs) for Requirement Clarification and Implementation Planning, and suggests implicit-requirement recovery as a key candidate direction for improving coding agents.
Xin Zhou, Chun-Yong Chong, Kisub Kim et al.· 0 citations
Large language model based coding agents have made substantial progress on repository-level software engineering tasks. Existing repository benchmarks, however, usually start from a human-identified issue and evaluate whether a patch satisfies a functional signal. We present SWE-Prometheus, a benchmark for the broader...
Jia-Jun Wu, Lei-Xin Sun, Zi-Hang Tan et al.· 0 citations
SWE Refactor Bench is introduced, a benchmark comprising 20 whole-repository migrations, covering 4 kinds of technical debt, and SWE Refactor Bench is positioned as a rigorous testbed for developing coding agents for reliable whole-repository migrations.
De-Yao Hong, Yi-Zhe Chi, Wen-Yi Li et al.· 3 citations· ⚡1
SWE-Bench Pro Verified offers a more trustworthy benchmark for assessing software engineering agents, which combines anti-hacking safeguards that eliminate major leakage channels without disrupting normal agent functionality, with task refinement that minimally corrects inconsistencies within flawed instances.
Pujun Zheng, Zi-Xin Shang, Shufan Jiang et al.· 1 citation
A paired ablation that removes explicit scientific guidance while preserving the repository and executable engineering context shows that scientific knowledge is not uniformly beneficial: well-grounded information can constrain repair and improve average performance and token efficiency, whereas poorly aligned guidance...
Zhi-Peng Xu, Jia-Hao Lu, Yi-Ning Zheng et al.· 4 citations
This work introduces PonyEval, a SWE-bench-style benchmark of 291 real GitHub issue-pull-request pairs from 15 Pony repositories that defines a matched evaluation with mini-SWE-agent 2.4.6 for GPT-5.6-sol, DeepSeek-V4-Pro, GLM-5.2, MiniMax-M3, and Kimi-K3, followed by strict patch application, compilation, and hidden-t...
Bang Xie, Hao Liu, Zhen-Yu Shi et al.· 0 citations
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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