AssertFlip: Reproducing Bugs via Inversion of LLM-Generated Passing Tests
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Abstract (arXiv)
Bug reproduction is critical in the software debugging and repair process, yet the majority of bugs in open-source and industrial settings lack executable tests to reproduce them at the time they are reported, making diagnosis and resolution more difficult and time-consuming. To address this challenge, we introduce AssertFlip, a novel technique for automatically generating Bug Reproducible Tests (BRTs) using large language models (LLMs). Unlike existing methods that attempt direct generation of failing tests, AssertFlip first generates passing tests on the buggy behaviour and then inverts these tests to fail when the bug is present. We hypothesize that LLMs are better at writing passing tests than ones that crash or fail on purpose. Our results show that AssertFlip outperforms all known techniques in the leaderboard of SWT-Bench, a benchmark curated for BRTs. Specifically, AssertFlip achieves a fail-to-pass success rate of 43.6% on the SWT-Bench-Verified subset.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Jul 2025.
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Citing papers per year: 2025: 1; 2026: 10.
11 papers.
- 2025Agentic Software Issue Resolution with Large Language Models: A Surveycited by 6
- 2026Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repaircited by 6
- 2026SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Testscited by 5
- 2026SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositoriescited by 4
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixescited by 3
- 2026EvoOtter: Evolutionary Reproduction Test Generatorcited by 1
- 2026ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reportscited by 1
- 2026SWE-Spot: Building Small Repo-Experts with Repository-Centric Learningcited by 0
- 2026Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?cited by 0
- 2026DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generationcited by 0
- 2026Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skillscited by 0
- 2026Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills10 Sep 2026
- 2026DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generation24 Aug 2026
- 2026Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?30 Jul 2026
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes22 Jul 2026
- 2026ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reports10 Jul 2026
- 2026EvoOtter: Evolutionary Reproduction Test Generator3 Jul 2026
- 2026SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests1 Jul 2026
- 2026SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning29 Jan 2026
- 2026Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair27 Jan 2026
- 2026SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories20 Jan 2026
- 2025Agentic Software Issue Resolution with Large Language Models: A Survey24 Dec 2025