Skip to content

SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents

Sep 2026 · 0 citations · 52 references
Computer Science

TL;DR

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.

View source

Similar papers

Preprint Aug 2026

A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents

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
#artificial intelligence Preprint Sep 2026

SWE-Prometheus: Measuring Engineering Governance Improvements in Real-World Repositories

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
#software testing Preprint Aug 2026

SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?

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
#artificial intelligence Preprint Sep 2026

SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents

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
Preprint Aug 2026

SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?

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
Review Aug 2026

PonyEval: Evaluating LLM-Based Program Repair for Capability-Safe and Actor-Oriented Pony Software

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

Related blog posts

MIT News · Artificial Intelligence Oct 2, 2026

Documenting the tech worker movement

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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.