# TSSB-3M: Mining single statement bugs at massive scale

arXiv: https://arxiv.org/abs/2201.12046 · HTML: https://mlsanity.com/paper/2201.12046/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2022.

## Built on by (most cited first, 14 of 14)

- [Large Language Models and Simple, Stupid Bugs](https://mlsanity.com/paper/2303.11455/) (2023)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Toward a Theory of Causation for Interpreting Neural Code Models](https://mlsanity.com/paper/2302.03788/) (2023)
- [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)
- [R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models](https://mlsanity.com/paper/2406.01359/) (2024)
- [Out of Context: How important is Local Context in Neural Program Repair?](https://mlsanity.com/paper/2312.04986/) (2023)
- [Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes](https://mlsanity.com/paper/2207.00301/) (2022)
- [The Grand Illusion: The Myth of Software Portability and Implications for ML Progress](https://mlsanity.com/paper/2309.07181/) (2023)
- [Where's the Bug? Attention Probing for Scalable Fault Localization](https://mlsanity.com/paper/2502.13966/) (2025)
- [Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?](https://mlsanity.com/paper/2308.08943/) (2023)
- [Insights from Benchmarking Frontier Language Models on Web App Code Generation](https://mlsanity.com/paper/2409.05177/) (2024)
- [Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs](https://mlsanity.com/paper/2601.10496/) (2026)
- [ExecRetrieval: Measuring the Functional-Correctness Gap in Code-Embedding Retrieval](https://mlsanity.com/paper/2609.01865/) (2026)

## Abstract (arXiv)

Single statement bugs are one of the most important ingredients in the evaluation of modern bug detection and automatic program repair methods. By affecting only a single statement, single statement bugs represent a type of bug often overlooked by developers, while still being small enough to be detected and fixed by automatic methods. With the rise of data-driven automatic repair the availability of single statement bugs at the scale of millionth of examples is more important than ever; not only for testing these methods but also for providing sufficient real world examples for training. To provide access to bug fix datasets of this scale, we are releasing two datasets called SSB-9M and TSSB-3M. While SSB-9M provides access to a collection of over 9M general single statement bug fixes from over 500K open source Python projects , TSSB-3M focuses on over 3M single statement bugs which can be fixed solely by a single statement change. To facilitate future research and empirical investigations, we annotated each bug fix with one of 20 single statement bug (SStuB) patterns typical for Python together with a characterization of the code change as a sequence of AST modifications. Our initial investigation shows that at least 40% of all single statement bug fixes mined fit at least one SStuB pattern, and that the majority of 72% of all bugs can be fixed with the same syntactic modifications as needed for fixing SStuBs.