How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset
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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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- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
- 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 106
- 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Softwarecited by 89
- 2021Self-Supervised Bug Detection and Repaircited by 59
- 2024Calibration and Correctness of Language Models for Codecited by 49
- 2023Large Language Models and Simple, Stupid Bugscited by 45
- 2023Uncovering the Limits of Machine Learning for Automatic Vulnerability Detectioncited by 30
- 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 28
- 2021On Multi-Modal Learning of Editing Source Codecited by 25
- 2021Understanding Neural Code Intelligence Through Program Simplificationcited by 20
- 2021Memorization and Generalization in Neural Code Intelligence Modelscited by 17
23 more
- 2022On Distribution Shift in Learning-based Bug Detectorscited by 15
- 2022Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptionscited by 11
- 2023RunBugRun -- An Executable Dataset for Automated Program Repaircited by 10
- 2022Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5cited by 9
- 2021Learning to Describe Solutions for Bug Reports Based on Developer Discussionscited by 5
- 2023Learning Defect Prediction from Unrealistic Datacited by 5
- 2023Out of Context: How important is Local Context in Neural Program Repair?cited by 5
- 2022Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixescited by 4
- 2025RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Modelscited by 4
- 2023CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Backcited by 3
- 2025Where's the Bug? Attention Probing for Scalable Fault Localizationcited by 3
- 2022JEMMA: An Extensible Java Dataset for ML4Code Applicationscited by 2
- 2021Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representationscited by 1
- 2023Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?cited by 1
- 2023Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairingcited by 1
- 2024Insights from Benchmarking Frontier Language Models on Web App Code Generationcited by 1
- 2025RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessmentcited by 1
- 2026Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMscited by 1
- 2021DeepMutants: Training neural bug detectors with contextual mutationscited by 0
- 2022BigIssue: A Realistic Bug Localization Benchmarkcited by 0
- 2023Graph Neural Networks For Mapping Variables Between Programs -- Extended Versioncited by 0
- 2023CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Codecited by 0
- 2026Toward an Agentic Infused Software Ecosystemcited by 0
- 2026Toward an Agentic Infused Software Ecosystem24 Feb 2026
- 2026Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs15 Jan 2026
- 2025RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models4 Sep 2025
- 2025RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment30 Jul 2025
- 2025Where's the Bug? Attention Probing for Scalable Fault Localization19 Feb 2025
- 2024Insights from Benchmarking Frontier Language Models on Web App Code Generation8 Sep 2024
- 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code1 Apr 2024
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repair25 Mar 2024
- 2024Calibration and Correctness of Language Models for Code3 Feb 2024
- 2023CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code20 Dec 2023
- 2023Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing16 Dec 2023
- 2023Out of Context: How important is Local Context in Neural Program Repair?8 Dec 2023
23 more
- 2023Learning Defect Prediction from Unrealistic Data2 Nov 2023
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?10 Oct 2023
- 2023Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?17 Aug 2023
- 2023Graph Neural Networks For Mapping Variables Between Programs -- Extended Version24 Jul 2023
- 2023Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection28 Jun 2023
- 2023Large Language Models of Code Fail at Completing Code with Potential Bugs6 Jun 2023
- 2023RunBugRun -- An Executable Dataset for Automated Program Repair3 Apr 2023
- 2023Large Language Models and Simple, Stupid Bugs20 Mar 2023
- 2023CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back8 Feb 2023
- 2022JEMMA: An Extensible Java Dataset for ML4Code Applications18 Dec 2022
- 2022Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT527 Nov 2022
- 2022BigIssue: A Realistic Bug Localization Benchmark21 Jul 2022
- 2022Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes1 Jul 2022
- 2022On Distribution Shift in Learning-based Bug Detectors21 Apr 2022
- 2022Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions7 Mar 2022
- 2021Learning to Describe Solutions for Bug Reports Based on Developer Discussions8 Oct 2021
- 2021On Multi-Modal Learning of Editing Source Code15 Aug 2021
- 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software19 Jul 2021
- 2021DeepMutants: Training neural bug detectors with contextual mutations14 Jul 2021
- 2021Memorization and Generalization in Neural Code Intelligence Models16 Jun 2021
- 2021Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations11 Jun 2021
- 2021Understanding Neural Code Intelligence Through Program Simplification7 Jun 2021
- 2021Self-Supervised Bug Detection and Repair26 May 2021
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
- 2026Toward an Agentic Infused Software Ecosystemcited by 0
- 2026Toward an Agentic Infused Software Ecosystem24 Feb 2026
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repair25 Mar 2024
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 106
- 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Softwarecited by 89
- 2021Self-Supervised Bug Detection and Repaircited by 59
- 2024Calibration and Correctness of Language Models for Codecited by 49
- 2023Large Language Models and Simple, Stupid Bugscited by 45
- 2023Uncovering the Limits of Machine Learning for Automatic Vulnerability Detectioncited by 30
- 2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 28
- 2021On Multi-Modal Learning of Editing Source Codecited by 25
- 2021Understanding Neural Code Intelligence Through Program Simplificationcited by 20
- 2021Memorization and Generalization in Neural Code Intelligence Modelscited by 17
- 2022On Distribution Shift in Learning-based Bug Detectorscited by 15
12 most cited of 32 All 32 (JSON)
- 2026Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs15 Jan 2026
- 2025RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models4 Sep 2025
- 2025Where's the Bug? Attention Probing for Scalable Fault Localization19 Feb 2025
- 2024Insights from Benchmarking Frontier Language Models on Web App Code Generation8 Sep 2024
- 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code1 Apr 2024
- 2024Calibration and Correctness of Language Models for Code3 Feb 2024
- 2023CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code20 Dec 2023
- 2023Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing16 Dec 2023
- 2023Out of Context: How important is Local Context in Neural Program Repair?8 Dec 2023
- 2023Learning Defect Prediction from Unrealistic Data2 Nov 2023
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?10 Oct 2023
- 2023Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?17 Aug 2023
12 newest of 32 All 32 (JSON)