SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
Pujun Zheng, Zixin Shang, Shufan Jiang, Wenhui Tian, Dongsheng Zhu, Zerun Ma, Dingbo Yuan, Qi Zhang
East China Normal University · Shanghai Artificial Intelligence Laboratory · Fudan University
Published September 8, 2026 · on the feed September 10, 2026 · ▲ 13 · arXiv 2609.08149
The abstract
SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysis work show that its evaluation is undermined by two sources of unreliability: reward hacking, enabled by leakage of gold solutions or hidden evaluation information, and task quality issues, including misleading problem statements and improperly scoped tests. These issues can inflate benchmark performance and obscure agents' true coding ability. We present SWE-Bench Pro Verified, a verified version of SWE-Bench Pro that addresses both problems. Our approach combines anti-hacking safeguards that eliminate major leakage channels without disrupting normal agent functionality, with task refinement that minimally corrects inconsistencies within flawed instances. Evaluations on SWE-Bench Pro Verified reveal that some models perform substantially worse than previously reported, suggesting that existing results on SWE-Bench Pro may overestimate real software engineering capability. SWE-Bench Pro Verified offers a more trustworthy benchmark for assessing software engineering agents.
The Brief
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Transcript
SWE-Bench Pro Verified. A Reliable Benchmark for Software Engineering Agents. By Pujun Zheng and seven others, from East China Normal University, the Shanghai Artificial Intelligence Laboratory, and one other institution. Published on the eighth of September. Its code repository is worked out: about three hundred and fifty source files, with a readme and a licence.
SWE-Bench Pro has become a standard way to evaluate software engineering agents on hard, repository-level tasks. The authors' analysis finds its evaluation undermined by two sources of unreliability. One is reward hacking, made possible when gold solutions or hidden evaluation information leak to the agent. The other is task quality: misleading problem statements, and tests that are improperly scoped. Both can inflate scores and hide how well an agent really codes.
The goal does not change. SWE-Bench Pro Verified still measures an agent's ability to solve real repository-level software engineering tasks. What changes is how much of a score can be trusted to reflect that ability, rather than a leak or a flawed task.
The verified version tackles both problems. Anti-hacking safeguards shut the major leakage channels, and they are designed to do so without disrupting normal agent functionality, so an honest agent works as before. Task refinement then corrects inconsistencies inside flawed instances, and it does so minimally, changing only what needs fixing so the tasks stay close to the originals.
Evaluated on the verified benchmark, some models perform substantially worse than previously reported. The authors take this to suggest that existing SWE-Bench Pro results may overestimate real software engineering capability. No specific figures appear in the abstract.
A benchmark's number is only meaningful if the path to it is the one intended. When the answers can leak and some tasks are broken, a high score measures the wrong thing. SWE-Bench Pro Verified keeps the same kind of task and closes the side doors, and the drop some models show is a sign of how much those side doors may have been adding.
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Learn it by doing: make the calls the authors made, guess the result before you see it, and defend it in a viva graded on your own words. Chapter 1 is right here.
Chapter 1 🧩 · The problem
Scores keep rising on a hard coding benchmark. The question is whether they mean anything.
You maintain a popular benchmark where coding agents fix real issues in large repositories. Scores keep climbing, and you start to wonder what they measure.
The road ahead
- 1 · The problem ~6 min A coding benchmark's rising scores may reflect agents finding the answer, or broken tasks, rather than real coding skill.
- 2 · What came before ~3 min
- 3 · The key idea ~9 min Trust the benchmark again by sealing the major routes to the answer and minimally repairing the tasks that are broken.
- 4 · Choose your own research ~3 min
- 5 · Build the method ~8 min Lock down the environment, then minimally repair flawed tasks, then re-run models on the result.
- 6 · The experiment ~6 min You’ll guess: When models were re-evaluated on SWE-Bench Pro Verified, how did their scores compare with previously reported SWE-Bench Pro results?
- 7 · The viva ~5 min Three questions from the examiners, answered in your own words. Win it for the paper’s trophy.
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