The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An in-depth study of the promises and perils of mining GitHub
Open accessAbstract
With over 10 million git repositories, GitHub is becoming one of the most important sources of software artifacts on the Internet. Researchers mine the information stored in GitHub’s event logs to understand how its users employ the site to collaborate on software, but so far there have been no studies describing the quality and properties of the available GitHub data. We document the results of an empirical study aimed at understanding the characteristics of the repositories and users in GitHub; we see how users take advantage of GitHub’s main features and how their activity is tracked on GitHub and related datasets to point out misalignment between the real and mined data. Our results indicate that while GitHub is a rich source of data on software development, mining GitHub for research purposes should take various potential perils into consideration. For example, we show that the majority of the projects are personal and inactive, and that almost 40 % of all pull requests do not appear as merged even though they were. Also, approximately half of GitHub’s registered users do not have public activity, while the activity of GitHub users in repositories is not always easy to pinpoint. We use our identified perils to see if they can pose validity threats; we review selected papers from the MSR 2014 Mining Challenge and see if there are potential impacts to consider. We provide a set of recommendations for software engineering researchers on how to approach the data in GitHub.
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Cites
Basics of Qualitative Research (3rd ed.): Techniques and Procedures for Developing Grounded Theory
Corbin, J., & Strauss, A. (2008) · SAGE · doi:10.4135/9781452230153 · Cited by 12,136
Social coding in GitHub: transparency and collaboration in an open software repository
Dabbish, L., Stuart, C., Tsay, J., & Herbsleb, J. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work · doi:10.1145/2145204.2145396 · Cited by 999
Cited by
How has forking changed in the last 20 years?: a study of hard forks on GitHub
Zhou, S., Vasilescu, B., & Kästner, C. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering · doi:10.1145/3377811.3380412 · Cited by 36
SourceFinder: Finding Malware Source-Code from Publicly Available Repositories
Rokon, M. O. F., Islam, R., Darki, A., Papalexakis, V. E., & Faloutsos, M. (2020) · arXiv · doi:10.48550/arxiv.2005.14311 · Cited by 10
Pitfalls and guidelines for using time-based Git data
Flint, S. W., Chauhan, J., & Dyer, R. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10200-y · Cited by 9
Component Mismatches Are a Critical Bottleneck to Fielding AI-Enabled Systems in the Public Sector
Lewis, G. A., Bellomo, S., & Galyardt, A. (2019) · arXiv · doi:10.48550/arxiv.1910.06136 · Cited by 8
Metadata
- Authors
- Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., & Damian, D.
- Venue
- Empirical Software Engineering
- Published
- 2015-09-05
- Volume
- 21
- Issue
- 5
- Pages
- 2035-2071
- ISSN
- 1382-3256 (ISSN-L)
- 1573-7616
- OpenAlex
- W1914969610
- Cited by
- 282
- References
- 2
- FWCI
- 32.43
- Type
- journal article
- Language
- en
- Open access
- green
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