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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20887
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dc.contributor.authorAzeem, Muhammad Ilyas-
dc.contributor.authorPanichella, Sebastiano-
dc.contributor.authorDi Sorbo, Andrea-
dc.contributor.authorSerebrenik, Alexander-
dc.contributor.authorWang, Qing-
dc.date.accessioned2020-11-25T10:17:01Z-
dc.date.available2020-11-25T10:17:01Z-
dc.date.issued2020-
dc.identifier.isbn9781450375122de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20887-
dc.description.abstractPull 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.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleAction-based recommendation in pull-request developmentde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1145/3379177.3388904de_CH
dc.identifier.doi10.21256/zhaw-20887-
zhaw.conference.detailsICSSP '20: International Conference on Software and System Processes, Seoul, South Korea, June 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end124de_CH
zhaw.pages.start115de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsProceedings of the International Conference on Software and System Processesde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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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