mlsanityData to 7 Oct 2026

Pynguin: Automated Unit Test Generation for Python

arXiv 2202.05218 ↗First posted 10 Feb 2022cs.SEcited by 49

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Abstract (arXiv)

Automated unit test generation is a well-known methodology aiming to reduce the developers' effort of writing tests manually. Prior research focused mainly on statically typed programming languages like Java. In practice, however, dynamically typed languages have received a huge gain in popularity over the last decade. This introduces the need for tools and research on test generation for these languages, too. We introduce Pynguin, an extendable test-generation framework for Python, which generates regression tests with high code coverage. Pynguin is designed to be easily usable by practitioners; it is also extensible to allow researchers to adapt it for their needs and to enable future research. We provide a demo of Pynguin at https://youtu.be/UiGrG25Vts0; further information, documentation, the tool, and its source code are available at https://www.pynguin.eu.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2022.

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Citing papers per year: 2022: 2; 2023: 3; 2024: 9; 2025: 14; 2026: 21.

49 papers.

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  1. 2022CodeT: Code Generation with Generated Testscited by 369
  2. 2024Large Language Model-Based Agents for Software Engineering: A Surveycited by 173
  3. 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agentscited by 129
  4. 2022Execution-Based Evaluation for Open-Domain Code Generationcited by 89
  5. 2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLMcited by 63
  6. 2024The Limits of Inference Scaling Through Resamplingcited by 51
  7. 2024CoverUp: Effective High Coverage Test Generation for Pythoncited by 36
  8. 2024The Future of AI-Driven Software Engineeringcited by 23
  9. 2025HardTests: Synthesizing High-Quality Test Cases for LLM Codingcited by 22
  10. 2023CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Executioncited by 21
  11. 2023LLM4DV: Using Large Language Models for Hardware Test Stimuli Generationcited by 21
  12. 2026SWE-Universe: Scale Real-World Verifiable Environments to Millionscited by 16
37 more
  1. 2023Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Toolscited by 13
  2. 2025A Contemporary Survey of Large Language Model Assisted Program Analysiscited by 10
  3. 2024B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Testscited by 9
  4. 2025LLM Test Generation via Iterative Hybrid Program Analysiscited by 9
  5. 2025TestForge: Feedback-Driven, Agentic Test Suite Generationcited by 8
  6. 2025Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generationcited by 8
  7. 2026LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loopscited by 8
  8. 2026Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generationcited by 6
  9. 2025Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?cited by 5
  10. 2025OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Researchcited by 3
  11. 2025Clarifying Semantics of In-Context Examples for Unit Test Generationcited by 3
  12. 2024Design choices made by LLM-based test generators prevent them from finding bugscited by 2
  13. 2025EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generationcited by 2
  14. 2025DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Patterncited by 2
  15. 2026Evaluating LLM-Based Test Generation Under Software Evolutioncited by 2
  16. 2024Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent Systemcited by 1
  17. 2025deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnessescited by 1
  18. 2025Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Modelscited by 1
  19. 2025LSPRAG: LSP-Guided RAG for Language-Agnostic Real-Time Unit Test Generationcited by 1
  20. 2026Enhancing LLM-Based Test Generation by Eliminating Covered Codecited by 1
  21. 2026Self-Improving Code Generation via Semantic Entropy and Behavioral Consensuscited by 1
  22. 2026Planning to Explore: Curiosity-Driven Planning for LLM Test Generationcited by 1
  23. 2026Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolutioncited by 1
  24. 2026LogicHunter: Testing LLM Agent Frameworks with an Agentic Oraclecited by 1
  25. 2026SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generationcited by 1
  26. 2025Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmapcited by 0
  27. 2026ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modulescited by 0
  28. 2026MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learningcited by 0
  29. 2026PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generationcited by 0
  30. 2026FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedbackcited by 0
  31. 2026MuMuTestUp: Mutation-based Multi-Agent Test Case Updatecited by 0
  32. 2026Rethinking Code Performance Benchmarks for LLMscited by 0
  33. 2026Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Testscited by 0
  34. 2026The Order Is the Guarantee: Verifier-Budgeted Code Deletion with Static-First Learned Proposalscited by 0
  35. 2026Ockhamareto: Pareto-Gated Segment-Level Credit Assignment for Concise Unit-Test Generation with Reinforcement Learningcited by 0
  36. 2026Grounded Skill Synthesis from Code at Scale for Agentic Intelligencecited by 0
  37. 2026Codoku: Renewable Program-Reasoning Challenges for Frontier Coding Agentscited by 0