DeepSoftwareAnalytics发布SWE-Gate基准，揭示AI编码助手通过功能测试≠满足实际开发要求
SWE-Gate基准测试评估软件工程代理时，不仅关注功能测试通过率，还检查代码审查约束合规性。该基准包含303个来自75个开源Python仓库的修复实例。实验显示，在644个通过功能测试的修复中，221个未能满足代码审查要求。这表明仅评估功能性会高估代理完成完整修复任务的能力。
SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents
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.