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research-article

An Empirical Study on GitHub Pull Requests’ Reactions

Published: 30 September 2023 Publication History

Abstract

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., “Thumbs-up”, “Laugh”, “Hooray”, “Heart”, “Rocket”, “Thumbs-down”, “Confused”, and “Eyes”. 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’ 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.
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., “Thumbs-up”, “Hooray”, “Heart”, “Rocket”, and “Laugh”). 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 “end-users” 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’s 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’ reactions.

References

[1]
Lin Bin, Zampetti Fiorella, Bavota Gabriele, Di Penta Massimiliano, Lanza Michele, and Oliveto Rocco. 2018. Sentiment analysis for software engineering: How far can we go? In Proceedings of the 2018 International Conference on Software Engineering. 94–104.
[2]
Felbo Bjarke, Mislove Alan, Søgaard Anders, Rahwan Iyad, and Lehmann Sune. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.
[3]
Jake Boxer. 2016. Add Reactions to Pull Requests, Issues, and Comments. Retrieved from https://github.blog/2016-03-10-add-reactions-to-pull-requests-issues-and-comments/. Accessed 30 May 2023.
[4]
Pletea Daniel, Vasilescu Bogdan, and Serebrenik Alexander. 2014. Security and emotion: Sentiment analysis of security discussions on GitHub. In Proceedings of the 2014 Working Conference on Mining Software Repositories. 348–351.
[5]
Gachechiladze Daviti, Lanubile Filippo, Novielli Nicole, and Serebrenik Alexander. 2017. Anger and its direction in collaborative software development. In Proceedings of the 2017 IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track. 11–14.
[6]
Davidov Dmitry, Tsur Oren, and Rappoport Ari. 2010. Enhanced sentiment learning using Twitter hashtags and smileys. In Proceedings of the Coling 2010: Posters. 241–249.
[7]
Biswas Eeshita, Karabulut M. Efruz, Pollock Lori, and Vijay-Shanker K. 2020. Achieving reliable sentiment analysis in the software engineering domain using BERT. In Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution. 162–173.
[8]
Skriptsova Ekaterina, Voronova Elizaveta, Danilova Elizaveta, and Bakhitova Alina. 2019. Analysis of newcomers activity in communicative posts on GitHub. In Proceedings of the 2019 International Conference on Digital Transformation and Global Society. 452–460.
[9]
Constantinou Eleni and Mens Tom. 2017. An empirical comparison of developer retention in the RubyGems and Npm software ecosystems. Innovations in Systems and Software Engineering 13 (2017), 101–115.
[10]
Guzman Emitza and Bruegge Bernd. 2013. Towards emotional awareness in software development teams. In Proceedings of the 2013 Joint Meeting on Foundations of Software Engineering. 671–674.
[11]
Huq S. Fatiul, Sadiq A. Zafar, and Sakib Kazi. 2019. Understanding the effect of developer sentiment on fix-inducing changes: An exploratory study on GitHub pull requests. In Proceedings of the 2019 Asia-Pacific Software Engineering Conference. 514–521.
[12]
Avelino Guilherme, Constantinou Eleni, Valente M. Tulio, and Serebrenik Alexander. 2019. On the abandonment and survival of open source projects: An empirical investigation. In Proceedings of the 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. 1–12.
[13]
Avelino Guilherme, Passos Leonardo, Hora Andre, and Valente M. Tulio. 2016. A novel approach for estimating truck factors. In Proceedings of the 2016 IEEE 24th International Conference on Program Comprehension. 1–10.
[14]
Pinto Gustavo, Steinmacher Igor, and Gerosa M. Aurélio. 2016. More common than you think: An in-depth study of casual contributors. In Proceedings of the 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering. 112–123.
[15]
Borges Hudson, Brito Rodrigo, and Valente M. Tulio. 2019. Beyond textual issues: Understanding the usage and impact of GitHub reactions. In Proceedings of the 2019 Brazilian Symposium on Software Engineering. 397–406.
[16]
Crowston Kevin, Wei Kangning, Li Qing, and Howison James. 2006. Core and periphery in free/libre and open source software team communications. In Proceedings of the 2006 Annual Hawaii International Conference on System Sciences. 118a–118a.
[17]
Sun Kexin, Gao Hui, Kuang Hongyu, Ma Xiaoxing, Rong Guoping, Shao Dong, and Zhang He. 2021. Exploiting the unique expression for improved sentiment analysis in software engineering text. In Proceedings of the 2021 IEEE/ACM International Conference on Program Comprehension. 149–159.
[18]
Krippendorff Klaus. 2004. Reliability in content analysis: Some common misconceptions and recommendations. Human Communication Research 30, 3 (2004), 411–433.
[19]
Xuan Lu, Yanbin Cao, Zhenpeng Chen, and Xuanzhe Liu. 2018. A first look at emoji usage on GitHub: An empirical study. arXiv:1812.04863. Retrieved from https://arxiv.org/abs/1812.04863.
[20]
Claes Maëlick, Mäntylä Mika, and Farooq Umar. 2018. On the use of emoticons in open source software development. In Proceedings of the 2018 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. 1–4.
[21]
Venigalla A.S. Manasa and Chimalakonda Sridhar. 2021. StackEmo: Towards enhancing user experience by augmenting stack overflow with emojis. In Proceedings of the 2021 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 1550–1554.
[22]
Ortu Marco, Adams Bram, Destefanis Giuseppe, Tourani Parastou, Marchesi Michele, and Tonelli Roberto. 2015. Are bullies more productive? empirical study of affectiveness vs. issue fixing time. In Proceedings of the 2015 IEEE/ACM Working Conference on Mining Software Repositories. 303–313.
[23]
Freira Mateus, Caetano Josemar, Oliveira Johnatan, and Marques-Neto Humbertor. 2018. Analyzing the impact of feedback in GitHub on the software developer’s mood. In Proceedings of the 2018 International Conference on Software Engineering and Knowledge Engineering. 445–450.
[24]
Justin Middleton, Emerson Murphy-Hill, Demetrius Green, Adam Meade, Roger Mayer, David White, and Steve McDonald. 2018. Which contributions predict whether developers are accepted into GitHub teams. In Proceedings of the 2018 IEEE/ACM International Conference on Mining Software Repositories. 403–413.
[25]
Mäntylä Mika, Adams Bram, Destefanis Giuseppe, Graziotin Daniel, and Ortu Marco. 2016. Mining valence, arousal, and dominance: Possibilities for detecting burnout and productivity?. In Proceedings of the 2016 International Conference on Mining Software Repositories. 247–258.
[26]
De Zoysa Nalin. 2020. Analysis of Textual and Non-Textual Sources of Sentiment in Github. Master’s thesis. University of Waterloo, Waterloo, Ontario, Canada.
[27]
Novielli Nicole, Girardi Daniela, and Lanubile Filippo. 2018. A benchmark study on sentiment analysis for software engineering research. In Proceedings of the 2018 IEEE/ACM International Conference on Mining Software Repositories. 364–375.
[28]
Bai Qiyu, Dan Qi, Mu Zhe, and Yang Maokun. 2019. A systematic review of emoji: Current research and future perspectives. Frontiers in Psychology 10 (2019), 2221.
[29]
Nainggolan Rena, Resianta Perangin-angin, Emma Simarmata, and Astuti F. Tarigan. 2019. Improved the performance of the K-means cluster using the sum of squared error (SSE) optimized by using the elbow method. In Proceedings of the Journal of Physics: Conference Series. 012–015.
[30]
Jongeling Robbert, Datta Subhajit, and Serebrenik Alexander. 2015. Choosing your weapons: On sentiment analysis tools for software engineering research. In Proceedings of the 2015 IEEE International Conference on Software Maintenance and Evolution. 531–535.
[31]
Souza Rodrigo and Silva Bruno. 2017. Sentiment analysis of Travis CI builds. In Proceedings of the 2017 IEEE/ACM International Conference on Mining Software Repositories. 459–462.
[32]
Joe Song and Haizhou Wang. 2020. Optimal (Weighted) Univariate Clustering. https://search.r-project.org/CRAN/refmans/Ckmeans.1d.dp/html/Ckmeans.1d.dp.html. Accessed 30 May 2023.
[33]
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. 46–55.
[34]
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? Identifying emotional expressions on Facebook reactions using clustering mechanism. IEEE Access 7 (2019), 53909–53921.
[35]
Son Teyon, Xiao Tao, Wang Dong, Kula R. Gaikovina, Ishio Takashi, and Matsumoto Kenichi. 2021. More than react: Investigating the role of emoji reaction in GitHub pull requests. arXiv:2108.08094. Retrieved from https://arxiv.org/abs/2108.08094.
[36]
Zhang Ting, Xu Bowen, Thung Ferdian, Haryono S. Agus, Lo David, and Jiang Lingxiao. 2020. Sentiment analysis for software engineering: How far can pre-trained transformer models go? In Proceedings of the 2020 IEEE International Conference on Software Maintenance and Evolution. 70–80.
[37]
Jason Tsay, Laura Dabbish, and James Herbsleb. 2014. Influence of social and technical factors for evaluating contribution in GitHub. In Proceedings of the 2014 International Conference on Software Engineering. 356–366.
[38]
Michal R. Wrobel. 2013. Emotions in the software development process. In Proceedings of the 2013 International Conference on Human System Interactions. 518–523.
[39]
Michal R. Wrobel. 2016. Towards the participant observation of emotions in software development teams. In Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. 1545–1548.
[40]
Xu Xinyuan, Manrique Ruben, and Pereira N. Bernardo. 2021. RIP emojis and words to contextualize mourning on Twitter. In Proceedings of the 2021 ACM Conference on Hypertext and Social Media. 257–263.
[41]
Haochao Ying, Liang Chen, Tingting Liang, and Jian Wu. 2016. Earec: Leveraging expertise and authority for pull-request reviewer recommendation in GitHub. In Proceedings of the 2016 IEEE/ACM International Workshop on CrowdSourcing in Software Engineering. 29–35.
[42]
Yue Yu, Huaimin Wang, Gang Yin, and Charles X. Ling. 2014. Who should review this pull-request: Reviewer recommendation to expedite crowd collaboration. In Proceedings of the 2014 Asia-Pacific Software Engineering Conference. 335–342.
[43]
Chen Zhenpeng, Lu Xuan, Ai Wei, Li Huoran, Mei Qiaozhu, and Liu Xuanzhe. 2018. Through a gender lens: Learning usage patterns of emojis from large-scale android users. In Proceedings of the 2018 World Wide Web Conference. 763–772.
[44]
Chen Zhenpeng, Cao Yanbin, Yao Huihan, Lu Xuan, Peng Xin, Mei Hong, and Liu Xuanzhe. 2021. Emoji-powered sentiment and emotion detection from software developers’ communication data. ACM Transactions on Software Engineering and Methodology 30, 2 (2021), 1–48.
[45]
Chen Zhenpeng, Cao Yanbin, Lu Xuan, Mei Qiaozhu, and Liu Xuanzhe. 2019. SEntiMoji: An emoji-powered learning approach for sentiment analysis in software engineering. In Proceedings of the 2019 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 841–852.
[46]
Huang Zijie, Shao Zhiqing, Fan Guisheng, Gao Jianhua, Zhou Ziyi, Yang Kang, and Yang Xingguang. 2021. Predicting community smells’ occurrence on individual developers by sentiments. In Proceedings of the 2021 IEEE/ACM International Conference on Program Comprehension. 230–241.

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    Published In

    cover image ACM Transactions on Software Engineering and Methodology
    ACM Transactions on Software Engineering and Methodology  Volume 32, Issue 6
    November 2023
    949 pages
    ISSN:1049-331X
    EISSN:1557-7392
    DOI:10.1145/3625557
    • Editor:
    • Mauro Pezzè
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 September 2023
    Online AM: 13 May 2023
    Accepted: 19 April 2023
    Revised: 23 February 2023
    Received: 20 December 2022
    Published in TOSEM Volume 32, Issue 6

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    Author Tags

    1. GitHub reactions
    2. pull requests
    3. software collaboration
    4. feedback

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    • (2024)On the Impact of Draft Pull Requests on Accelerating Feedback2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58944.2024.00056(550-561)Online publication date: 6-Oct-2024
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