The Impact of Collaboration Patterns and Network Centrality on Long-Term Contribution in GitHub Project
Source abstract
Abstract
Open-source software (OSS) projects rely on collaborative contributions, yet sustaining long-term engagement remains a challenge. This study examines how contributor activity frequency and network centrality impact sustained contributions in 672 GitHub repositories. Using k-core decomposition and model checking, we find that contributors with higher k-core values manage more pull requests. Additionally, they show greater resilience in continuing to contribute after pull request rejections, a behavior rarely seen in non-core contributors. Moreover, the likelihood of long-term engagement increases significantly when contributors’ pull requests are processed swiftly, with delayed or rejected pull requests resulting in higher disengagement, particularly among peripheral contributors. We also observe that as project networks grow more complex over time, core contributors become essential in maintaining project sustainability. These findings offer new insights into fostering core contributor retention and underscore the need for efficient governance and PR management practices in OSS projects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Open Source Software Innovations · Scientific Computing and Data Management · Software Engineering Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of SW Safety and Security, Kyonggi University, Suwon-si 154-42, Gyeonggi-do, Republic of Korea
Department of Computer Science, Kyonggi University, Suwon-si 154-42, Gyeonggi-do, Republic of Korea
Funding
Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT)
References (34)
Show source-provided references
1. The transformation of open source software10.2307/25148740 ↗
2. Achieving quality in open-source software10.1109/MS.2007.2 ↗
3. McDonald, N., and Goggins, S. (May, January 27). Performance and participation in open source software on github. Proceedings of the CHI’13 Extended Abstracts on Human Factors in Computing Systems, Paris, France.
4. Tsay, J., Dabbish, L., and Herbsleb, J. (, January 16–21November). Let’s talk about it: Evaluating contributions through discussion in GitHub. Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, Hong Kong, China.
5. Perez-Riverol, Y., Gatto, L., Wang, R., Sachsenberg, T., Uszkoreit, J., Leprevost, F.d.V., Fufezan, C., Ternent, T., Eglen, S.J., and Katz, D.S. (2016). Ten simple rules for taking advantage of Git and GitHub. PLoS Comput. Biol., 12.10.1101/048744 ↗
6. Elazhary, O., Storey, M.A., Ernst, N., and Zaidman, A. (October, January 29). Do as i do, not as i say: Do contribution guidelines match the github contribution process?. Proceedings of the 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), Cleveland, OH, USA.10.1109/ICSME.2019.00043 ↗
7. The social structure of free and open source software development
8. Ait, A., Izquierdo, J.L.C., and Cabot, J. (2022, January 23–24). An empirical study on the survival rate of GitHub projects. Proceedings of the 19th International Conference on Mining Software Repositories, Pittsburgh, PA, USA.10.1145/3524842.3527941 ↗
9. Two case studies of open source software development: Apache and Mozilla10.1145/567793.567795 ↗
10. Effort, co-operation and co-ordination in an open source software project: GNOME10.1046/j.1365-2575.2002.00110.x ↗
11. Xia, X., Weng, Z., Wang, W., and Zhao, S. (2022, January 6–9). Exploring activity and contributors on github: Who, what, when, and where. Proceedings of the 2022 29th Asia-Pacific Software Engineering Conference (APSEC), Virtual.10.1109/APSEC57359.2022.00013 ↗
12. Discrete time Markov chains with interval probabilities10.1016/j.ijar.2009.06.007 ↗
13. Kwiatkowska, M., Norman, G., and Parker, D. (2022, January 14–17). PRISM: Probabilistic symbolic model checker. Proceedings of the International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, London, UK.
14. Kwiatkowska, M., Norman, G., and Parker, D. (2018). Probabilistic model checking: Advances and applications. Formal System Verification: State-of the-Art and Future Trends, Springer.10.1007/978-3-319-57685-5_3 ↗
15. Katoen, J.P. (2016, January 5–8). The probabilistic model checking landscape. Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science, New York, NY, USA.10.1145/2933575.2934574 ↗
16. Jo, S., Kwon, R., and Kwon, G. (2024). Probabilistic Model Checking GitHub Repositories for Software Project Analysis. Appl. Sci., 14.10.3390/app14031260 ↗
17. Tsay, J., Dabbish, L., and Herbsleb, J. (June, January 31). Influence of social and technical factors for evaluating contribution in GitHub. Proceedings of the 36th International Conference on Software Engineering, Hyderabad, India.
18. Gousios, G., Pinzger, M., and Deursen, A.v. (June, January 31). An exploratory study of the pull-based software development model. Proceedings of the 36th International Conference on Software Engineering, Hyderabad, India.
19. Who will stay in the floss community? modeling participant’s initial behavior10.1109/TSE.2014.2349496 ↗
20. Joblin, M., Apel, S., Hunsen, C., and Mauerer, W. (2017, January 20–28). Classifying developers into core and peripheral: An empirical study on count and network metrics. Proceedings of the 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), Buenos Aires, Argentina.10.1109/ICSE.2017.23 ↗
21. Rodrigues, F.A. (2019). Network centrality: An introduction. A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems, Springer.10.1007/978-3-319-78512-7_10 ↗
22. Brandes, U. (2005). Network Analysis: Methodological Foundations, Springer Science & Business Media.10.1007/b106453 ↗
23. A survey on optimization studies of group centrality metrics10.1002/net.22248 ↗
24. Reliable accuracy estimates from k-fold cross validation10.1109/TKDE.2019.2912815 ↗
25. Decision tree methods: Applications for classification and prediction
26. Montgomery, D.C., Peck, E.A., and Vining, G.G. (2021). Introduction to Linear Regression Analysis, John Wiley & Sons.
27. Guo, G., Wang, H., Bell, D., Bi, Y., and Greer, K. (2003, January 3–7). KNN model-based approach in classification. Proceedings of the on the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Italy. Proceedings.10.1007/978-3-540-39964-3_62 ↗
28. Tutorial on support vector machine (svm)
29. Natekin, A., and Knoll, A. (2013). Gradient boosting machines, a tutorial. Front. Neurorobotics, 7.10.3389/fnbot.2013.00021 ↗
30. Chen, T. (Xgboost: Extreme Gradient Boosting, 2015). Xgboost: Extreme Gradient Boosting, R Package version 0.4-2.
31. A random forest guided tour10.1007/s11749-016-0481-7 ↗
32. Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not10.5194/gmd-15-5481-2022 ↗
33. A unified approach to interpreting model predictions
34. Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost10.1016/j.compenvurbsys.2022.101845 ↗