mlsanityData to 7 Oct 2026

How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset

arXiv 1905.13334 ↗First posted 30 May 2019cs.SE cs.PLcited by 35

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Abstract (arXiv)

Program repair is an important but difficult software engineering problem. One way to achieve acceptable performance is to focus on classes of simple bugs, such as bugs with single statement fixes, or that match a small set of bug templates. However, it is very difficult to estimate the recall of repair techniques for simple bugs, as there are no datasets about how often the associated bugs occur in code. To fill this gap, we provide a dataset of 153,652 single statement bug-fix changes mined from 1,000 popular open-source Java projects, annotated by whether they match any of a set of 16 bug templates, inspired by state-of-the-art program repair techniques. In an initial analysis, we find that about 33% of the simple bug fixes match the templates, indicating that a remarkable number of single-statement bugs can be repaired with a relatively small set of templates. Further, we find that template fitting bugs appear with a frequency of about one bug per 1,600-2,500 lines of code (as measured by the size of the project's latest version). We hope that the dataset will prove a resource for both future work in program repair and studies in empirical software engineering.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2019.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 12; 2024: 4; 2025: 3; 2026: 2.

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  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
  3. 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 106
  4. 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Softwarecited by 89
  5. 2021Self-Supervised Bug Detection and Repaircited by 59
  6. 2024Calibration and Correctness of Language Models for Codecited by 49
  7. 2023Large Language Models and Simple, Stupid Bugscited by 45
  8. 2023Uncovering the Limits of Machine Learning for Automatic Vulnerability Detectioncited by 30
  9. 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 28
  10. 2021On Multi-Modal Learning of Editing Source Codecited by 25
  11. 2021Understanding Neural Code Intelligence Through Program Simplificationcited by 20
  12. 2021Memorization and Generalization in Neural Code Intelligence Modelscited by 17
23 more
  1. 2022On Distribution Shift in Learning-based Bug Detectorscited by 15
  2. 2022Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptionscited by 11
  3. 2023RunBugRun -- An Executable Dataset for Automated Program Repaircited by 10
  4. 2022Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5cited by 9
  5. 2021Learning to Describe Solutions for Bug Reports Based on Developer Discussionscited by 5
  6. 2023Learning Defect Prediction from Unrealistic Datacited by 5
  7. 2023Out of Context: How important is Local Context in Neural Program Repair?cited by 5
  8. 2022Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixescited by 4
  9. 2025RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Modelscited by 4
  10. 2023CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Backcited by 3
  11. 2025Where's the Bug? Attention Probing for Scalable Fault Localizationcited by 3
  12. 2022JEMMA: An Extensible Java Dataset for ML4Code Applicationscited by 2
  13. 2021Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representationscited by 1
  14. 2023Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?cited by 1
  15. 2023Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairingcited by 1
  16. 2024Insights from Benchmarking Frontier Language Models on Web App Code Generationcited by 1
  17. 2025RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessmentcited by 1
  18. 2026Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMscited by 1
  19. 2021DeepMutants: Training neural bug detectors with contextual mutationscited by 0
  20. 2022BigIssue: A Realistic Bug Localization Benchmarkcited by 0
  21. 2023Graph Neural Networks For Mapping Variables Between Programs -- Extended Versioncited by 0
  22. 2023CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Codecited by 0
  23. 2026Toward an Agentic Infused Software Ecosystemcited by 0