# IncBL: Incremental Bug Localization

arXiv: https://arxiv.org/abs/2106.07413 · HTML: https://mlsanity.com/paper/2106.07413/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2021.

## Built on by (most cited first, 4 of 4)

- [GypSum: Learning Hybrid Representations for Code Summarization](https://mlsanity.com/paper/2204.12916/) (2022)
- [LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis](https://mlsanity.com/paper/2601.05539/) (2026)
- [Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing](https://mlsanity.com/paper/2312.10448/) (2023)
- [SLICEMATE: Accurate and Scalable Static Program Slicing via LLM-Powered Agents](https://mlsanity.com/paper/2507.18957/) (2025)

## Abstract (arXiv)

Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on average compared with the re-computing the model, while maintaining the same level of accuracy. We also implement IncBL as a Github App that can be easily integrated into open-source projects on GitHub. Users can deploy and use IncBL locally as well. The demo video for IncBL can be viewed at https://youtu.be/G4gMuvlJSb0, and the source code can be found at https://github.com/soarsmu/IncBL.