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

arXiv: https://arxiv.org/abs/1905.13334 · HTML: https://mlsanity.com/paper/1905.13334/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 30 of 36)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code](https://mlsanity.com/paper/2404.00971/) (2024)
- [CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software](https://mlsanity.com/paper/2107.08760/) (2021)
- [Self-Supervised Bug Detection and Repair](https://mlsanity.com/paper/2105.12787/) (2021)
- [Calibration and Correctness of Language Models for Code](https://mlsanity.com/paper/2402.02047/) (2024)
- [Large Language Models and Simple, Stupid Bugs](https://mlsanity.com/paper/2303.11455/) (2023)
- [Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection](https://mlsanity.com/paper/2306.17193/) (2023)
- [Large Language Models of Code Fail at Completing Code with Potential Bugs](https://mlsanity.com/paper/2306.03438/) (2023)
- [On Multi-Modal Learning of Editing Source Code](https://mlsanity.com/paper/2108.06645/) (2021)
- [Understanding Neural Code Intelligence Through Program Simplification](https://mlsanity.com/paper/2106.03353/) (2021)
- [Memorization and Generalization in Neural Code Intelligence Models](https://mlsanity.com/paper/2106.08704/) (2021)
- [On Distribution Shift in Learning-based Bug Detectors](https://mlsanity.com/paper/2204.10049/) (2022)
- [Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions](https://mlsanity.com/paper/2203.03771/) (2022)
- [RunBugRun -- An Executable Dataset for Automated Program Repair](https://mlsanity.com/paper/2304.01102/) (2023)
- [Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5](https://mlsanity.com/paper/2211.14875/) (2022)
- [Learning to Describe Solutions for Bug Reports Based on Developer Discussions](https://mlsanity.com/paper/2110.04353/) (2021)
- [Learning Defect Prediction from Unrealistic Data](https://mlsanity.com/paper/2311.00931/) (2023)
- [Out of Context: How important is Local Context in Neural Program Repair?](https://mlsanity.com/paper/2312.04986/) (2023)
- [RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models](https://mlsanity.com/paper/2509.04078/) (2025)
- [Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes](https://mlsanity.com/paper/2207.00301/) (2022)
- [CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back](https://mlsanity.com/paper/2302.03924/) (2023)
- [Where's the Bug? Attention Probing for Scalable Fault Localization](https://mlsanity.com/paper/2502.13966/) (2025)
- [JEMMA: An Extensible Java Dataset for ML4Code Applications](https://mlsanity.com/paper/2212.09132/) (2022)
- [Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations](https://mlsanity.com/paper/2106.06110/) (2021)
- [Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?](https://mlsanity.com/paper/2308.08943/) (2023)
- [Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing](https://mlsanity.com/paper/2312.10448/) (2023)
- [Insights from Benchmarking Frontier Language Models on Web App Code Generation](https://mlsanity.com/paper/2409.05177/) (2024)
- [RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment](https://mlsanity.com/paper/2507.22580/) (2025)
- [Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs](https://mlsanity.com/paper/2601.10496/) (2026)

## 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.