mlsanityData to 14 Oct 2026

Are "Solved Issues" in SWE-bench Really Solved Correctly? An Empirical Study

arXiv 2503.15223 ↗First posted 19 Mar 2025cs.SEcited by 49

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Cited by 38 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Automated issue solving aims to resolve real-world issues in software repositories. The most popular benchmarks for automated issue solving are SWE-bench and its human-filtered subset SWE-bench Verified. These benchmarks leverage testing to validate generated patches. However, because testing is rarely exhaustive, a patch may pass the tests but nevertheless fail to match the developers' expectations. Unfortunately, it is currently unclear to what extent evaluations performed with SWE-bench suffer from such plausible but incorrect patches. This paper presents an in-depth empirical study of the correctness of plausible patches generated by three state-of-the-art issue-solving tools evaluated on SWE-bench Verified. We extensively test and inspect generated patches, and compare them against human-written ground truth patches. The core of our methodology is a novel technique PatchDiff for differential patch testing, which automatically exposes behavioral discrepancies between two patches. Our findings reveal critical weaknesses in SWE-bench's patch validation mechanism, which causes 7.8% of all patches to count as correct while failing the developer-written test suite. Moreover, our novel automated technique reveals that even more (29.6%) plausible patches induce different behavior than the ground truth patches. These behavioral differences are often due to similar, but divergent implementations (46.8%) and due to generated patches that adapt more behavior than the ground truth patches (27.3%). Our manual inspection shows that 28.6% of behaviorally divergent patches are certainly incorrect. Combined, the different weaknesses lead to an inflation of reported resolution rates by 6.2 absolute percent points. Our findings are a call to arms for more robust and reliable evaluation of issue-solving tools. We envision our automated differential patch testing technique to be useful for this purpose.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 15; 2026: 34.

49 papers.

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  1. 2025Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scalingcited by 94
  2. 2025SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolutioncited by 53
  3. 2026Code as Agent Harnesscited by 47
  4. 2025Agentic Software Engineering: Foundational Pillars and a Research Roadmapcited by 41
  5. 2025Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectoriescited by 33
  6. 2026Agentic Much? Adoption of Coding Agents on GitHubcited by 32
  7. 2025Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Studycited by 16
  8. 2025Does SWE-Bench-Verified Test Agent Ability or Model Memory?cited by 16
  9. 2025A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic Systemcited by 15
  10. 2026BenchGuard: Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarkscited by 15
  11. 2025SPICE: An Automated SWE-Bench Labeling Pipeline for Issue Clarity, Test Coverage, and Effort Estimationcited by 12
  12. 2026Agent psychometrics: Task-level performance prediction in agentic coding benchmarkscited by 11
37 more
  1. 2025SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarkscited by 7
  2. 2025InfCode: Adversarial Iterative Refinement of Tests and Patches for Reliable Software Issue Resolutioncited by 7
  3. 2025Agentic Software Issue Resolution with Large Language Models: A Surveycited by 6
  4. 2026Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repaircited by 6
  5. 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agentscited by 6
  6. 2026Agentic Hardware Design as Repository-Level Code Evolutioncited by 6
  7. 2025SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solvingcited by 5
  8. 2025How Safe Are AI-Generated Patches? A Large-scale Study on Security Risks in LLM and Agentic Automated Program Repair on SWE-benchcited by 5
  9. 2026Can Agent Benchmarks Support Their Scores? Evidence-Supported Bounds for Interactive-Agent Evaluationcited by 5
  10. 2026What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agentscited by 5
  11. 2026From Patches to Trajectories: Privileged Process Supervision for Software-Engineering Agentscited by 4
  12. 2026Building to the Test: Coding Agents Deliver What You Check, Not What You Requestedcited by 4
  13. 2026SWE-Prime: Fewer Trajectories, Better Performancecited by 4
  14. 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixescited by 3
  15. 2026ExecCritic: Learn to Test, Test to Improve for Coding Agentscited by 3
  16. 2026SkillForge: Self-Distilling Agents for Project-Specific Issue Resolutioncited by 2
  17. 2026CodeMidas: Scaling Agentic Coding RL Environments from Code Itselfcited by 2
  18. 2025AutoCodeSherpa: Symbolic Explanations in AI Coding Agentscited by 1
  19. 2026Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?cited by 1
  20. 2026LLM-Oriented Information Retrieval: A Denoising-First Perspectivecited by 1
  21. 2026PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarkscited by 1
  22. 2026SpecPath: Testing Coding Agents Across Contract-Equivalent Specification Historiescited by 1
  23. 2026Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluationcited by 1
  24. 2026Before the Rollout Ends: Early Terminal Reward Prediction for Long-horizon Coding Agentscited by 1
  25. 2026Groundability, Not Scale Alone: When Weak Reviewers Can Audit Strong Coding Agentscited by 1
  26. 2025Diff-XYZ: A Benchmark for Evaluating Diff Understandingcited by 0
  27. 2026SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimizationcited by 0
  28. 2026Reducing Token Usage of State-in-Context Agents using Minificationcited by 0
  29. 2026LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolutioncited by 0
  30. 2026Agent Safety Should Be a Runtime Contractcited by 0
  31. 2026Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineeringcited by 0
  32. 2026Schr"odinger's Code Repository: Have LLMs Learned SWE-bench or Memorized It?cited by 0
  33. 2026RECLAIM: Can Agents Reproduce the Claims of Machine Learning Papers?cited by 0
  34. 2026Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agentscited by 0
  35. 2026Incident-Arena: Getting agents to the last nine of reliabilitycited by 0
  36. 2026Where Did the Repair First Go Wrong? Localizing the Origins of Silent Failures in Agentic Vulnerability Repaircited by 0
  37. 2026Re-executing Coding-Agent Evaluations: Unchanged Decisions and Unresolved Policy Riskcited by 0