# AssertFlip: Reproducing Bugs via Inversion of LLM-Generated Passing Tests

arXiv: https://arxiv.org/abs/2507.17542 · HTML: https://mlsanity.com/paper/2507.17542/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2025.

## Built on by (most cited first, 11 of 11)

- [Agentic Software Issue Resolution with Large Language Models: A Survey](https://mlsanity.com/paper/2512.22256/) (2025)
- [Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair](https://mlsanity.com/paper/2601.19066/) (2026)
- [SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests](https://mlsanity.com/paper/2607.00990/) (2026)
- [SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories](https://mlsanity.com/paper/2601.13713/) (2026)
- [Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes](https://mlsanity.com/paper/2607.19843/) (2026)
- [EvoOtter: Evolutionary Reproduction Test Generator](https://mlsanity.com/paper/2607.02854/) (2026)
- [ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reports](https://mlsanity.com/paper/2607.09123/) (2026)
- [SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning](https://mlsanity.com/paper/2601.21649/) (2026)
- [Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?](https://mlsanity.com/paper/2607.28871/) (2026)
- [DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generation](https://mlsanity.com/paper/2608.23341/) (2026)
- [Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills](https://mlsanity.com/paper/2609.12012/) (2026)

## 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.