# PreciseBugCollector: Extensible, Executable and Precise Bug-fix Collection

arXiv: https://arxiv.org/abs/2309.06229 · HTML: https://mlsanity.com/paper/2309.06229/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories](https://mlsanity.com/paper/2511.00197/) (2025)
- [Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2406.05892/) (2024)
- [An Exploratory Study on LLM-Generated Code and Comments in Code Repositories](https://mlsanity.com/paper/2607.01867/) (2026)

## Abstract (arXiv)

Bug datasets are vital for enabling deep learning techniques to address software maintenance tasks related to bugs. However, existing bug datasets suffer from precise and scale limitations: they are either small-scale but precise with manual validation or large-scale but imprecise with simple commit message processing. In this paper, we introduce PreciseBugCollector, a precise, multi-language bug collection approach that overcomes these two limitations. PreciseBugCollector is based on two novel components: a) A bug tracker to map the codebase repositories with external bug repositories to trace bug type information, and b) A bug injector to generate project-specific bugs by injecting noise into the correct codebases and then executing them against their test suites to obtain test failure messages. We implement PreciseBugCollector against three sources: 1) A bug tracker that links to the national vulnerability data set (NVD) to collect general-wise vulnerabilities, 2) A bug tracker that links to OSS-Fuzz to collect general-wise bugs, and 3) A bug injector based on 16 injection rules to generate project-wise bugs. To date, PreciseBugCollector comprises 1057818 bugs extracted from 2968 open-source projects. Of these, 12602 bugs are sourced from bug repositories (NVD and OSS-Fuzz), while the remaining 1045216 project-specific bugs are generated by the bug injector. Considering the challenge objectives, we argue that a bug injection approach is highly valuable for the industrial setting, since project-specific bugs align with domain knowledge, share the same codebase, and adhere to the coding style employed in industrial projects.