{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:59:02Z","timestamp":1783097942971,"version":"3.54.6"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T00:00:00Z","timestamp":1696032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>The pull request mechanism is commonly used to propose source code modifications and get feedback from the community before merging them into a software repository. On GitHub, practitioners can provide feedback on a pull request by either commenting on the pull request or simply reacting to it using a set of pre-defined GitHub reactions, i.e., \u201cThumbs-up\u201d, \u201cLaugh\u201d, \u201cHooray\u201d, \u201cHeart\u201d, \u201cRocket\u201d, \u201cThumbs-down\u201d, \u201cConfused\u201d, and \u201cEyes\u201d. While a large number of prior studies investigated how to improve different software engineering activities (e.g., code review and integration) by investigating the feedback on pull requests, they focused only on pull requests\u2019 comments as a source of feedback. However, the GitHub reactions, according to our preliminary study, contain feedback that is not manifested within the comments of pull requests. In fact, our preliminary analysis of six popular projects shows that a median of 100% of the practitioners who reacted to a pull request did not leave any comment suggesting that reactions can be a unique source of feedback to further improve the code review and integration process.<\/jats:p>\n          <jats:p>To help future studies better leverage reactions as a feedback mechanism, we conduct an empirical study to understand the usage of GitHub reactions and understand their promises and limitations. We investigate in this article how reactions are used, when and who use them on what types of pull requests, and for what purposes. Our study considers a quantitative analysis on a set of 380 k reactions on 63 k pull requests of six popular open-source projects on GitHub and three qualitative analyses on a total number of 989 reactions from the same six projects. We find that the most common used GitHub reactions are the positive ones (i.e., \u201cThumbs-up\u201d, \u201cHooray\u201d, \u201cHeart\u201d, \u201cRocket\u201d, and \u201cLaugh\u201d). We observe that reactors use positive reactions to express positive attitude (e.g., approval, appreciation, and excitement) on the proposed changes in pull requests. A median of just 1.95% of the used reactions are negative ones, which are used by reactors who disagree with the proposed changes for six reasons, such as feature modifications that might have more downsides than upsides or the use of the wrong approach to address certain problems. Most (a median of 78.40%) reactions on a pull request come before the closing of the corresponding pull requests. Interestingly, we observe that non-contributors (i.e., outsiders who potentially are the \u201cend-users\u201d of the software) are also active on reacting to pull requests. On top of that, we observe that core contributors, peripheral contributors, casual contributors and outsiders have different behaviors when reacting to pull requests. For instance, most core contributors react in the early stages of a pull request, while peripheral contributors, casual contributors and outsiders react around the closing time or, in some cases, after a pull request is merged. Contributors tend to react to the pull request\u2019s source code, while outsiders are more concerned about the impact of the pull request on the end-user experience. Our findings shed light on common patterns of GitHub reactions usage on pull requests and provide taxonomies about the intention of reactors, which can inspire future studies better leverage pull requests\u2019 reactions.<\/jats:p>","DOI":"10.1145\/3597208","type":"journal-article","created":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T11:15:54Z","timestamp":1683976554000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["An Empirical Study on GitHub Pull Requests\u2019 Reactions"],"prefix":"10.1145","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5534-6426","authenticated-orcid":false,"given":"Mohamed Amine","family":"Batoun","sequence":"first","affiliation":[{"name":"\u00c9cole de Technologie Sup\u00e9rieure, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9154-4667","authenticated-orcid":false,"given":"Ka Lai","family":"Yung","sequence":"additional","affiliation":[{"name":"Queen\u2019s University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2208-3893","authenticated-orcid":false,"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Queen\u2019s University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2724-0034","authenticated-orcid":false,"given":"Mohammed","family":"Sayagh","sequence":"additional","affiliation":[{"name":"\u00c9cole de Technologie Sup\u00e9rieure, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"94","volume-title":"Proceedings of the 2018 International Conference on Software Engineering","author":"Bin Lin","year":"2018","unstructured":"Lin Bin, Zampetti Fiorella, Bavota Gabriele, Di Penta Massimiliano, Lanza Michele, and Oliveto Rocco. 2018. 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Accessed 30 May 2023."},{"key":"e_1_3_2_34_2","first-page":"46","volume-title":"Proceedings of the 2019 International Conference on Predictive Models and Data Analytics in Software Engineering","author":"Song Wang","year":"2019","unstructured":"Wang Song, Chetan Bansal, Nachiappan Nagappan, and Adithya A. Philip. 2019. Leveraging change intents for characterizing and identifying large-review-effort changes. In Proceedings of the 2019 International Conference on Predictive Models and Data Analytics in Software Engineering. 46\u201355."},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"53909","DOI":"10.1109\/ACCESS.2019.2913136","article-title":"How do I feel? Identifying emotional expressions on Facebook reactions using clustering mechanism","author":"Taliar Giuntini F.","year":"2019","unstructured":"Giuntini F. Taliar, Larissa Pires, Luziane D. F. Kirchner, Denise A. Passarelli, Maria D. J. D. Dos Reis, Andrew T. Campbell, and Jo Ueyama. 2019. How do I feel? 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