The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Understanding the Factors That Impact the Popularity of GitHub Repositories
Open accessAbstract
Software popularity is a valuable information to modern open source developers, who constantly want to know if their systems are attracting new users, if new releases are gaining acceptance, or if they are meeting user's expectations. In this paper, we describe a study on the popularity of software systems hosted at GitHub, which is the world's largest collection of open source software. GitHub provides an explicit way for users to manifest their satisfaction with a hosted repository: the stargazers button. In our study, we reveal the main factors that impact the number of stars of GitHub projects, including programming language and application domain. We also study the impact of new features on project popularity. Finally, we identify four main patterns of popularity growth, which are derived after clustering the time series representing the number of stars of 2,279 popular GitHub repositories. We hope our results provide valuable insights to developers and maintainers, which could help them on building and evolving systems in a competitive software market.
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(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958
Gordon, A. D., & Hartigan, J. A. (1976) · Journal of the American Statistical Association · doi:10.2307/2286880 · Cited by 2,569
Two case studies of open source software development: Apache and Mozilla
Mockus, A., Fielding, R. T., & Herbsleb, J. D. (2002) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/567793.567795 · Cited by 1,685
Patterns of temporal variation in online media
Yang, J., & Leskovec, J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935863 · Cited by 986
The promises and perils of mining GitHub
Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., & Damian, D. (2014) · Proceedings of the 11th Working Conference on Mining Software Repositories · doi:10.1145/2597073.2597074 · Cited by 770
An exploratory study of the pull-based software development model
Gousios, G., Pinzger, M., & Deursen, A. V. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568260 · Cited by 628
Capacity Planning for Web Services: Metrics, Models, and Methods
Menascé, D. A., & Almeida, V. A. F. (2001) · Cited by 408
Dynamical classes of collective attention in twitter
Lehmann, J., Gonçalves, B., Ramasco, J. J., & Cattuto, C. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187871 · Cited by 380
Why people hate your app: making sense of user feedback in a mobile app store
Fu, B., Lin, J., Li, L., Faloutsos, C., Hong, J., & Sadeh, N. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2488202 · Cited by 370
Work practices and challenges in pull-based development: the contributor's perspective
Gousios, G., Storey, M.-A., & Bacchelli, A. (2016) · Proceedings of the 38th International Conference on Software Engineering · doi:10.1145/2884781.2884826 · Cited by 335
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Studying test-driven development and its retainment over a six-month time span
Baldassarre, M. T., Caivano, D., Fucci, D., Juristo, N., Romano, S., Scanniello, G., & Turhan, B. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110937 · Cited by 15
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Metadata
- Authors
- Borges, H., Hora, A., & Valente, M. T.
- Venue
- 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- 334-344
- OpenAlex
- W2440056063
- Cited by
- 312
- References
- 33
- FWCI
- 17.66
- Type
- conference paper
- Language
- en
- Open access
- green
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- Borges, H., Hora, A., & Valente, M. T. (2016). Understanding the Factors That Impact the Popularity of GitHub Repositories. 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME). https://doi.org/10.1109/icsme.2016.31
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