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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
Proceedings: Proceedings of the International Conference on Software and System Processes
Pages: 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.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

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