The Importance of Attributes in Predicting the Lifetime of Human and Automated Pull Requests

• Leandro Ferrarezi Valiante UFAC
• Mairieli Wessel Radboud University
• Manoel Limeira de Lima Júnior UFAC

DOI: https://doi.org/10.5753/wbots.2024.3826

Resumo

Pull request(PR)-based workflows improves collaboration in software development; however, the influx of PRs in certain repositories is challenging. Bots, like Dependabot, automate PR creation but can cause communication noise, indicating the need for smarter tools. We investigated 197, 736 PRs from 90 open-source projects using regression algorithms to predict PR lifetimes. Results showed that in 21 repositories, Dependabot PRs were reviewed faster, whereas in 47 repositories, human PRs were quicker. The RMSE difference was notable, with 18, 338 minutes for human PRs and 5, 585 minutes for Dependabot PRs. Key attributes for prediction were similar across scenarios, although the number of commits was very important only for Dependabot PRs.

Referências

Alfadel, M., Costa, D. E., Shihab, E., and Mkhallalati, M. (2021). On the use of dependabot security pull requests. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), pages 254–265.

Chacon, S. and Straub, B. (2014). Pro git. Springer Nature.

de Lima Júnior, M. L., Soares, D., Plastino, A., and Murta, L. (2021). Predicting the lifetime of pull requests in open-source projects. Journal of Software: Evolution and Process, 33(6):e2337.

de Lima Júnior, M. L., Soares, D. M., Plastino, A., and Murta, L. (2018). Automatic assignment of integrators to pull requests: The importance of selecting appropriate attributes. J. Syst. Softw., 144:181–196.

e Silva, J. M. and de Lima Júnior, M. L. (2021). Prediction of pull requests review time in open source projects. In Proceedings of the XX Brazilian Symposium on Software Quality, pages 1–10.

Ferrarezi, L. (2023). Data used for the research ‘The Importance of Attributes in Predicting the Lifetime of Human and Automated Pull Requests’. [link]. [Online: Accessed on 07/04/2024].

GitHub Docs (2022). Rate limits for the REST API. [link]. [Online: Accessed on 07/04/2024].

Gousios, G., Pinzger, M., and Deursen, A. v. (2014). An exploratory study of the pull-based software development model. In Proceedings of the 36th international conference on software engineering, pages 345–355.

Gousios, G. and Zaidman, A. (2014a). A dataset for pull-based development research. In Proceedings of the 11th Working Conference on Mining Software Repositories, pages 368–371.

Gousios, G. and Zaidman, A. (2014b). Pullreq Analysis. [link]. [Online: Accessed on 07/04/2024].

He, R., He, H., Zhang, Y., and Zhou, M. (2023). Automating dependency updates in practice: An exploratory study on github dependabot. IEEE Transactions on Software Engineering.

Khanan, C., Luewichana, W., Pruktharathikoon, K., Jiarpakdee, J., Tantithamthavorn, C., Choetkiertikul, M., Ragkhitwetsagul, C., and Sunetnanta, T. (2020). Jitbot: an explainable just-in-time defect prediction bot. In Proceedings of the 35th IEEE/ACM international conference on automated software engineering, pages 1336–1339.

Lessmann, S., Baesens, B., Mues, C., and Pietsch, S. (2008). Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Transactions on Software Engineering, 34(4):485–496.

Mirhosseini, S. and Parnin, C. (2017). Can automated pull requests encourage software developers to upgrade out-of-date dependencies? In 2017 32nd IEEE/ACM international conference on automated software engineering (ASE), pages 84–94. IEEE.

Monperrus, M. (2019). Explainable software bot contributions: Case study of automated bug fixes. In 2019 IEEE/ACM 1st international workshop on bots in software engineering (BotSE), pages 12–15. IEEE.

Nasrabadi, H. M., Agaronian, A. E., Zannone, N., Constantinou, E., and Serebrenik, A. (2023). Investigating the resolution of vulnerable dependencies with dependabot security updates. In Mining Software Repositories conference.

Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., and Herrera, F. (2017). A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing, 239:39–57.

Scikit Learn (2024). OneClassSVM. [link]. [Online: Accessed on 07/04/2024].

Silva, D. A. N. d., Soares, D. M., and Gonçalves, S. A. (2020). Measuring unique changes: How do distinct changes affect the size and lifetime of pull requests? In Proceedings of the 14th Brazilian Symposium on Software Components, Architectures, and Reuse, pages 121–130.

Soares, D. M., de Lima Júnior, M. L., Murta, L., and Plastino, A. (2021). What factors influence the lifetime of pull requests? Software: Practice and Experience, 51(6):1173–1193.

Wessel, M., De Souza, B. M., Steinmacher, I., Wiese, I. S., Polato, I., Chaves, A. P., and Gerosa, M. A. (2018). The power of bots: Characterizing and understanding bots in oss projects. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW):1–19.

Wyrich, M. and Bogner, J. (2019). Towards an autonomous bot for automatic source code refactoring. In 2019 IEEE/ACM 1st international workshop on bots in software engineering (BotSE), pages 24–28. IEEE.

Wyrich, M., Ghit, R., Haller, T., and Müller, C. (2021). Bots don’t mind waiting, do they? comparing the interaction with automatically and manually created pull requests. In 2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE), pages 6–10. IEEE.

Wyrich, M., Hebig, R., Wagner, S., and Scandariato, R. (2020). Perception and acceptance of an autonomous refactoring bot. arXiv preprint arXiv:2001.02553, 1:303–310.

Yu, Y., Wang, H., Filkov, V., Devanbu, P., and Vasilescu, B. (2015). Wait for it: Determinants of pull request evaluation latency on github. In 2015 IEEE/ACM 12th working conference on mining software repositories, pages 367–371. IEEE.