mlsanityData to 7 Oct 2026

ChatUniTest: A Framework for LLM-Based Test Generation

arXiv 2305.04764 ↗First posted 8 May 2023cs.SEcited by 94

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Cited by 34 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Unit testing is an essential yet frequently arduous task. Various automated unit test generation tools have been introduced to mitigate this challenge. Notably, methods based on large language models (LLMs) have garnered considerable attention and exhibited promising results in recent years. Nevertheless, LLM-based tools encounter limitations in generating accurate unit tests. This paper presents ChatUniTest, an LLM-based automated unit test generation framework. ChatUniTest incorporates an adaptive focal context mechanism to encompass valuable context in prompts and adheres to a generation-validation-repair mechanism to rectify errors in generated unit tests. Subsequently, we have developed ChatUniTest Core, a common library that implements core workflow, complemented by the ChatUniTest Toolchain, a suite of seamlessly integrated tools enhancing the capabilities of ChatUniTest. Our effectiveness evaluation reveals that ChatUniTest outperforms TestSpark and EvoSuite in half of the evaluated projects, achieving the highest overall line coverage. Furthermore, insights from our user study affirm that ChatUniTest delivers substantial value to various stakeholders in the software testing domain. ChatUniTest is available at https://github.com/ZJU-ACES-ISE/ChatUniTest, and the demo video is available at https://www.youtube.com/watch?v=GmfxQUqm2ZQ.

Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2023.

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Citing papers per year: 2023: 2; 2024: 10; 2025: 46; 2026: 36.

94 papers.

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  1. 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agentscited by 129
  2. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  3. 2024The Limits of Inference Scaling Through Resamplingcited by 51
  4. 2025Co-Evolving LLM Coder and Unit Tester via Reinforcement Learningcited by 51
  5. 2023WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Modelscited by 37
  6. 2024CoverUp: Effective High Coverage Test Generation for Pythoncited by 36
  7. 2025Mutation-Guided LLM-based Test Generation at Metacited by 23
  8. 2025QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checkscited by 22
  9. 2025Frontier AI's Impact on the Cybersecurity Landscapecited by 21
  10. 2025Agentic Bug Reproduction for Effective Automated Program Repair at Googlecited by 20
  11. 2025Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Taskscited by 20
  12. 2026SWE-Universe: Scale Real-World Verifiable Environments to Millionscited by 16
38 more
  1. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  2. 2026SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agentscited by 12
  3. 2025A Contemporary Survey of Large Language Model Assisted Program Analysiscited by 10
  4. 2025Rethinking the Evaluation of Secure Code Generationcited by 10
  5. 2024B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Testscited by 9
  6. 2024Multi-Programming Language Sandbox for LLMscited by 9
  7. 2025LLM Test Generation via Iterative Hybrid Program Analysiscited by 9
  8. 2024A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suitescited by 8
  9. 2024A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexioncited by 8
  10. 2025TestForge: Feedback-Driven, Agentic Test Suite Generationcited by 8
  11. 2025Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generationcited by 8
  12. 2024Retrieval-Augmented Test Generation: How Far Are We?cited by 7
  13. 2025CLOVER: A Test Case Generation Benchmark with Coverage, Long-Context, and Verificationcited by 7
  14. 2025Evaluating LLM-Based Regression Test Generationcited by 6
  15. 2025Multi-Agent Collaboration for Multilingual Code Instruction Tuningcited by 6
  16. 2025REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinementcited by 6
  17. 2025CluCERT: Certifying LLM Robustness via Clustering-Guided Denoising Smoothingcited by 6
  18. 2026Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generationcited by 6
  19. 2026Are Coding Agents Generating Over-Mocked Tests? An Empirical Studycited by 5
  20. 2026Can Vision-Language Models Handle Long-Context Code? An Empirical Study on Visual Compressioncited by 5
  21. 2025Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Modelscited by 4
  22. 2025SCGAgent: Recreating the Benefits of Reasoning Models for Secure Code Generation with Agentic Workflowscited by 4
  23. 2025AppForge: From Assistant to Independent Developer -- Are GPTs Ready for Software Development?cited by 4
  24. 2026TerraFormer: Automated Infrastructure-as-Code with LLMs Fine-Tuned via Policy-Guided Verifier Feedbackcited by 4
  25. 2025Towards Practical Defect-Focused Automated Code Reviewcited by 3
  26. 2026ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generationcited by 3
  27. 2024Generative transformations and patterns in LLM-native approaches for software verification and falsificationcited by 2
  28. 2024Are Human Rules Necessary? Generating Reusable APIs with CoT Reasoning and In-Context Learningcited by 2
  29. 2025EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generationcited by 2
  30. 2025Large Language Models for Software Testing: A Research Roadmapcited by 2
  31. 2025HarnessLLM: Automatic Testing Harness Generation via Reinforcement Learningcited by 2
  32. 2026VIBEPASS: Can Vibe Coders Really Pass the Vibe Check?cited by 2
  33. 2025Analysis of Student-LLM Interaction in a Software Engineering Projectcited by 1
  34. 2025A Systematic Approach for Assessing Large Language Models' Test Case Generation Capabilitycited by 1
  35. 2025High-Fidelity And Complex Test Data Generation For Google SQL Code Generation Servicescited by 1
  36. 2025Towards Automated Formal Verification of Backend Systems with LLMscited by 1
  37. 2025deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnessescited by 1
  38. 2025OptiHive: Ensemble Selection for LLM-Based Optimization via Statistical Modelingcited by 1

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