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https://doi.org/10.21256/zhaw-20887| Publication type: | Conference paper |
| Type of review: | Not specified |
| Title: | Action-based recommendation in pull-request development |
| Authors: | Azeem, Muhammad Ilyas Panichella, Sebastiano Di Sorbo, Andrea Serebrenik, Alexander Wang, Qing |
| et. al: | No |
| DOI: | 10.1145/3379177.3388904 10.21256/zhaw-20887 |
| Proceedings: | Proceedings of the International Conference on Software and System Processes |
| Page(s): | 115 |
| Pages to: | 124 |
| Conference details: | ICSSP '20: International Conference on Software and System Processes, Seoul, South Korea, June 2020 |
| Issue Date: | 2020 |
| Publisher / Ed. Institution: | Association for Computing Machinery |
| ISBN: | 9781450375122 |
| Language: | English |
| Subject (DDC): | 005: Computer programming, programs and data |
| Abstract: | Pull requests (PRs) selection is a challenging task faced by integrators in pull-based development (PbD), with hundreds of PRs submitted on a daily basis to large open-source projects. Managing these PRs manually consumes integrators' time and resources and may lead to delays in the acceptance, response, or rejection of PRs that can propose bug fixes or feature enhancements. On the one hand, well-known platforms for performing PbD, like GitHub, do not provide built-in recommendation mechanisms for facilitating the management of PRs. On the other hand, prior research on PRs recommendation has focused on the likelihood of either a PR being accepted or receive a response by the integrator. In this paper, we consider both those likelihoods, this to help integrators in the PRs selection process by suggesting to them the appropriate actions to undertake on each specific PR. To this aim, we propose an approach, called CARTESIAN (aCceptance And Response classificaTion-based requESt IdentificAtioN) modeling the PRs recommendation according to PR actions. In particular, CARTESIAN is able to recommend three types of PR actions: accept, respond, and reject. We evaluated CARTESIAN on the PRs of 19 popular GitHub projects. The results of our study demonstrate that our approach can identify PR actions with an average precision and recall of about 86%. Moreover, our findings also highlight that CARTESIAN outperforms the results of two baseline approaches in the task of PRs selection. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20887 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Azeem-etal_Action-based-recommendation-in-pull-request-development_ICSSP.pdf | Accepted Version | 293.88 kB | Adobe PDF |  View/Open |
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Azeem, M. I., Panichella, S., Di Sorbo, A., Serebrenik, A., & Wang, Q. (2020). Action-based recommendation in pull-request development [Conference paper]. Proceedings of the International Conference on Software and System Processes, 115–124. https://doi.org/10.1145/3379177.3388904
Azeem, M.I. et al. (2020) ‘Action-based recommendation in pull-request development’, in Proceedings of the International Conference on Software and System Processes. Association for Computing Machinery, pp. 115–124. Available at: https://doi.org/10.1145/3379177.3388904.
M. I. Azeem, S. Panichella, A. Di Sorbo, A. Serebrenik, and Q. Wang, “Action-based recommendation in pull-request development,” in Proceedings of the International Conference on Software and System Processes, 2020, pp. 115–124. doi: 10.1145/3379177.3388904.
AZEEM, Muhammad Ilyas, Sebastiano PANICHELLA, Andrea DI SORBO, Alexander SEREBRENIK und Qing WANG, 2020. Action-based recommendation in pull-request development. In: Proceedings of the International Conference on Software and System Processes. Conference paper. Association for Computing Machinery. 2020. S. 115–124. ISBN 9781450375122
Azeem, Muhammad Ilyas, Sebastiano Panichella, Andrea Di Sorbo, Alexander Serebrenik, and Qing Wang. 2020. “Action-Based Recommendation in Pull-Request Development.” Conference paper. In Proceedings of the International Conference on Software and System Processes, 115–24. Association for Computing Machinery. https://doi.org/10.1145/3379177.3388904.
Azeem, Muhammad Ilyas, et al. “Action-Based Recommendation in Pull-Request Development.” Proceedings of the International Conference on Software and System Processes, Association for Computing Machinery, 2020, pp. 115–24, https://doi.org/10.1145/3379177.3388904.
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