Developers in open source projects must make decisions on contributions from other community members, such as whether or not to accept a pull request. However, secondary factors—beyond the code itself—can influence those decisions. For example, signals from GitHub profiles, such as a number of followers, activity, names, or gender can also be considered when developers make decisions. In this paper, we examine how developers use these signals (or not) when making decisions about code contributions. To evaluate this question, we evaluate how signals related to perceived gender identity and code quality influenced decisions on accepting pull requests. Unlike previous work, we analyze this decision process with data collected from an eye-tracker. We analyzed differences in what signals developers said are important for themselves versus what signals they actually used to make decisions about others. We found that after the code snippet (x=57%), the second place programmers spent their time fixating on supplemental technical signals(x=32%), such as previous contributions and popular repositories. Diverging from what participants reported themselves, we also found that programmers fixated on social signals more than recalled.
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Assessing Perceived Sentiment in Pull Requests with Emoji: Evidence from Tools and Developer Eye Movements
The paper presents an eye tracking pilot study on understanding how developers read and assess sentiment in twenty-four GitHub pull requests containing emoji randomly selected from five different open source applications. Gaze data was collected on various elements of the pull request page in Google Chrome while the developers were tasked with determining perceived sentiment. The developer perceived sentiment was compared with sentiment output from five state-of-the-art sentiment analysis tools. SentiStrength-SE had the highest performance, with 55.56% of its predictions being agreed upon by study participants. On the other hand, Stanford CoreNLP fared the worst, with only 5.56% of its predictions matching that of the participants'. Gaze data shows the top three areas that developers looked at the most were the comment body, added lines of code, and username (the person writing the comment). The results also show high attention given to emoji in the pull request comment body compared to the rest of the comment text. These results can help provide additional guidelines on the pull request review process.
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- Award ID(s):
- 1855756
- NSF-PAR ID:
- 10267633
- Date Published:
- Journal Name:
- 2021 IEEE/ACM Sixth International Workshop on Emotion Awareness in Software Engineering (SEmotion)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SEmotion52567.2021.00009
