Action-based recommendation in Pull-request development

Muhammad Ilyas Azeem, Sebastiano Panichella, Andrea Di Sorbo, Alexander Serebrenik, Qing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of ICSSP 2020: International Conference on Software And System Processes
Pages115-124
Number of pages10
ISBN (Electronic)9781450375122
DOIs
Publication statusPublished - 26 Jun 2020
Event2020 International Conference on Software and System Processes (ICSSP 2020) - Seoul Dragon City Hotel, Seoul, Korea, Republic of
Duration: 23 May 202024 May 2020

Conference

Conference2020 International Conference on Software and System Processes (ICSSP 2020)
CountryKorea, Republic of
CitySeoul
Period23/05/2024/05/20

Keywords

  • Machine learning
  • Pull Requests recommendation
  • Software maintenance and evolution

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