An Empirical Study on GitHub Pull Requests’ Reactions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».