SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark
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Abstract (arXiv)
The SWE-Bench Verified leaderboard is approaching saturation, with the top system achieving 78.80%. However, we show that this performance is inflated. Our re-evaluation reveals that one in five "solved" patches from the top-30 agents are semantically incorrect, passing only because weak test suites fail to expose their errors. We present SWE-ABS, an adversarial framework that strengthens test suites through a two-stage pipeline: (1) coverage-driven augmentation using program slicing to target untested code regions, and (2) mutation-driven adversarial testing that synthesizes plausible but incorrect patches to expose semantic blind spots. On SWE-Bench Verified (500 instances), SWE-ABS strengthens 50.2% of instances, a 25.1x improvement over prior work, and rejects 19.71% of previously passing patches. As a result, the top agent's score decreases from 78.80% to 62.20%, leading to significant leaderboard reshuffling, with the previous top-ranked agent dropping to fifth place.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2026.
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Citing papers per year: 2026: 11.
11 papers.
- 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Taskscited by 24
- 2026Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJackcited by 13
- 2026Auditing Reward Hackability in Code RL Training Environmentscited by 3
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixescited by 2
- 2026SkillForge: Self-Distilling Agents for Project-Specific Issue Resolutioncited by 2
- 2026PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarkscited by 1
- 2026Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Modelcited by 1
- 2026What Does an Agentic Software Engineering Benchmark Measure? Profiling Task Demands and Agent Behaviour Beyond What Category Labels Revealcited by 0
- 2026Schr"odinger's Code Repository: Have LLMs Learned SWE-bench or Memorized It?cited by 0
- 2026Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agentscited by 0
- 2026TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolutioncited by 0
- 2026TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution7 Oct 2026
- 2026Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents27 Sep 2026
- 2026What Does an Agentic Software Engineering Benchmark Measure? Profiling Task Demands and Agent Behaviour Beyond What Category Labels Reveal1 Sep 2026
- 2026Schr"odinger's Code Repository: Have LLMs Learned SWE-bench or Memorized It?21 Aug 2026
- 2026SkillForge: Self-Distilling Agents for Project-Specific Issue Resolution19 Aug 2026
- 2026Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model14 Aug 2026
- 2026PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks30 Jul 2026
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes22 Jul 2026
- 2026Auditing Reward Hackability in Code RL Training Environments14 Jun 2026
- 2026Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack12 May 2026
- 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks25 Mar 2026