This is the first bug-fix pattern study in the Autonomous Driving System (ADS) domain. Our proposed hierarchical classification of ADS bug-fix pattern study includes 15 syntactic and 27 semantic bug-fix patterns, as well as root causes, symptoms, modules, sub-modules, related algorithms, and detailed bug-fix actions. This taxonomy is instrumental for developers, testers, and researchers in developing automated bug detection and repair tools for ADS.
The published paper (currently unreleased) of this ADS Bug-Fix Pattern Study will be available at . Now you can access the pre-print version of this paper at
The artifacts of this project are also available at
|--DIR_ROOT
|--data
|--bfp_label
|--csv
|--json
|--bug-fix file-change dataset
|--ApolloAuto_apollo
|--autowarefoundation_autoware
|--autowarefoundation_autoware.universe
|--pr_files.json
|--figs
data/bfp_label
contains the labeled data of pull requests from Apollo and Autoware GitHub repositories in two formats.data/bug-fix file-change dataset
contains the bug-fix code changes of pull requests.figs
contains the output figures generated by data analysis and data visualization.
This paper is accepted by FSE 2025, which is not published yet. Currently, we provide an arXiv version of this paper. We will update the paper citation once it is published.
If you use the data or code in this repository, please cite the following paper:
@article{chen2025comprehensive,
title={A Comprehensive Study of Bug-Fix Patterns in Autonomous Driving Systems},
author={Chen, Yuntianyi and Huai, Yuqi and He, Yirui and Li, Shilong and Hong, Changnam and Chen, Qi Alfred and Garcia, Joshua},
journal={arXiv preprint arXiv:2502.01937},
year={2025}
}