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

• URL: http://arxiv.org/abs/2507.17542v1
• Date: Wed, 23 Jul 2025 14:19:55 GMT
• Title: AssertFlip: Reproducing Bugs via Inversion of LLM-Generated Passing Tests
• Authors: Lara Khatib, Noble Saji Mathews, Meiyappan Nagappan,
• Abstract summary: We introduce AssertFlip, a technique for automatically generating Bug Reproducible Tests (BRTs) using large language models (LLMs)<n>AssertFlip first generates passing tests on the buggy behaviour and then inverts these tests to fail when the bug is present.<n>Our results show that AssertFlip outperforms all known techniques in the leaderboard of SWT-Bench, a benchmark curated for BRTs.
• Score: 0.7564784873669823
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• ProjectTest: A Project-level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms [48.43237545197775]
  Unit test generation has become a promising and important use case of LLMs.<n>ProjectTest is a project-level benchmark for unit test generation covering Python, Java, and JavaScript.
  arXiv  Detail & Related papers  (2025-02-10T15:24:30Z)
• Learning to Generate Unit Tests for Automated Debugging [52.63217175637201]
  Unit tests (UTs) play an instrumental role in assessing code correctness as well as providing feedback to large language models (LLMs)<n>We propose UTGen, which teaches LLMs to generate unit test inputs that reveal errors along with their correct expected outputs.<n>We show that UTGen outperforms other LLM-based baselines by 7.59% based on a metric measuring the presence of both error-revealing UT inputs and correct UT outputs.
  arXiv  Detail & Related papers  (2025-02-03T18:51:43Z)
• Design choices made by LLM-based test generators prevent them from finding bugs [0.850206009406913]
  This paper critically examines whether recent LLM-based test generation tools, such as Codium CoverAgent and CoverUp, can effectively find bugs or unintentionally validate faulty code.<n>Using real human-written buggy code as input, we evaluate these tools, showing how LLM-generated tests can fail to detect bugs and, more alarmingly, how their design can worsen the situation by validating bugs in the generated test suite and rejecting bug-revealing tests.
  arXiv  Detail & Related papers  (2024-12-18T18:33:26Z)
• TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration [7.509927117191286]
  Large language models (LLMs) have demonstrated remarkable capabilities in generating unit test cases.<n>We propose TestART, a novel unit test generation method.<n>TestART improves LLM-based unit testing via co-evolution of automated generation and repair iteration.
  arXiv  Detail & Related papers  (2024-08-06T10:52:41Z)
• STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay [76.06127233986663]
  Test-time adaptation (TTA) aims to address the distribution shift between the training and test data with only unlabeled data at test time. This paper pays attention to the problem that conducts both sample recognition and outlier rejection during inference while outliers exist. We propose a new approach called STAble Memory rePlay (STAMP), which performs optimization over a stable memory bank instead of the risky mini-batch.
  arXiv  Detail & Related papers  (2024-07-22T16:25:41Z)
• Leveraging Stack Traces for Spectrum-based Fault Localization in the Absence of Failing Tests [44.13331329339185]
  We introduce a new approach, SBEST, that integrates stack trace data with test coverage to enhance fault localization. Our approach shows a significant improvement, increasing Mean Average Precision (MAP) by 32.22% and Mean Reciprocal Rank (MRR) by 17.43% over traditional stack trace ranking methods.
  arXiv  Detail & Related papers  (2024-05-01T15:15:52Z)
• GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? [50.53312866647302]
  HateCheck is a suite for testing fine-grained model functionalities on synthesized data. We propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch. Crowd-sourced annotation demonstrates that the generated test cases are of high quality.
  arXiv  Detail & Related papers  (2024-02-23T10:02:01Z)
• DebugBench: Evaluating Debugging Capability of Large Language Models [80.73121177868357]
  DebugBench is a benchmark for Large Language Models (LLMs) It covers four major bug categories and 18 minor types in C++, Java, and Python. We evaluate two commercial and four open-source models in a zero-shot scenario.
  arXiv  Detail & Related papers  (2024-01-09T15:46:38Z)
• Automatic Generation of Test Cases based on Bug Reports: a Feasibility Study with Large Language Models [4.318319522015101]
  Existing approaches produce test cases that either can be qualified as simple (e.g. unit tests) or that require precise specifications. Most testing procedures still rely on test cases written by humans to form test suites. We investigate the feasibility of performing this generation by leveraging large language models (LLMs) and using bug reports as inputs.
  arXiv  Detail & Related papers  (2023-10-10T05:30:12Z)
• Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing [13.743062498008555]
  We introduce MuTAP for improving the effectiveness of test cases generated by Large Language Models (LLMs) in terms of revealing bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs) Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets.
  arXiv  Detail & Related papers  (2023-08-31T08:48:31Z)
• Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction [14.444294152595429]
  The number of tests added in open source repositories due to issues was about 28% of the corresponding project test suite size. We propose LIBRO, a framework that uses Large Language Models (LLMs), which have been shown to be capable of performing code-related tasks. Our evaluation of LIBRO shows that, on the widely studied Defects4J benchmark, LIBRO can generate failure reproducing test cases for 33% of all studied cases.
  arXiv  Detail & Related papers  (2022-09-23T10:50:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.