Painting the Automotive Software Landscape in GitHub

This dataset comprises of the raw data that we used for analyzing the automotive software landscape on GitHub. 'all topics from search queries' comprises of the all the topics from our initial search to identify automotive software on GitHub. 'selected_topics' consists of the final list of topics that were used to identify automotive software repositories on GitHub. 'Data from GitHub via PyGitHub' comprises of a curated list of automotive software projects, its associated meta data, and tags that classify it on 4 dimensions: (a) in-vehicle vs. tools, (b) safety critical vs. non-safety critical, (c) safety-critical based on application, and (d) broy's classification. 'auto_projects_data' and 'nonauto_projects_data' comprises of the development activities of automotive software and non-automotive software repositories respectively as derived from the GHTorrent data. For ease, the csv files follow the same schema as in the original GHTorrent data and can be directly imported in the mysql format for analysis. How can I cite this work? If you find this dataset useful and want to use it in your work, please cite the following paper: @inproceedings{kochanthara2022, title={Painting the Landscape of Automotive Software in GitHub}, author={Kochanthara, Sangeeth and Dajsuren, Yanja and Cleophas, Loek and van den Brand, Mark}, booktitle={The 2022 Mining Software Repositories Conference}, url={https://arxiv.org/pdf/2203.08936.pdf} year={2022}