BugsInPy: A Database of Existing Bugs in Python Programs to Enable Controlled Testing and Debugging Studies
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Abstract (arXiv)
The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further widened in the 2020 edition of the survey. Unfortunately, despite the rapid increase in Python's popularity, there are not many testing and debugging tools that are designed for Python. This is in stark contrast with the abundance of testing and debugging tools for Java. Thus, there is a need to push research on tools that can help Python developers. One factor that contributed to the rapid growth of Java testing and debugging tools is the availability of benchmarks. A popular benchmark is the Defects4J benchmark; its initial version contained 357 real bugs from 5 real-world Java programs. Each bug comes with a test suite that can expose the bug. Defects4J has been used by hundreds of testing and debugging studies and has helped to push the frontier of research in these directions. In this project, inspired by Defects4J, we create another benchmark database and tool that contain 493 real bugs from 17 real-world Python programs. We hope our benchmark can help catalyze future work on testing and debugging tools that work on Python programs.
Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2024.
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Citing papers per year: 2024: 9; 2025: 12; 2026: 14.
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- 2024Order Matters! An Empirical Study on Large Language Models' Input Order Bias in Software Fault Localization25 Dec 2024
- 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects13 Dec 2024
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- 2024Large Language Model-Based Agents for Software Engineering: A Survey4 Sep 2024
- 2024CoverUp: Effective High Coverage Test Generation for Python24 Mar 2024
- 2024Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing19 Feb 2024
- 2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM31 Jan 2024
- 2024Large Language Model-Based Agents for Software Engineering: A Surveycited by 173
- 2024HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scalecited by 61
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- 2025SLICEMATE: Accurate and Scalable Static Program Slicing via LLM-Powered Agents25 Jul 2025
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- 2025SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugscited by 22
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- 2024Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testingcited by 5
- 2025Assessing the Impact of Code Changes on the Fault Localizability of Large Language Modelscited by 4
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- 2026Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)24 Jul 2026
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- 2026An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code25 Apr 2026
- 2026Tricky2: Towards a Benchmark for Evaluating Human and LLM Error Interactions26 Jan 2026
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- 2025SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs20 Apr 2025
- 2025Assessing the Impact of Code Changes on the Fault Localizability of Large Language Models6 Apr 2025
12 newest of 21 All 21 (JSON)