How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub
Software engineering methodologies rely on version control systems such as git to store source code artifacts and manage changes to the codebase. Pull requests include chunks of source code, history of changes, log messages around a proposed change of the mainstream codebase, and much discussion on...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9117137/ |
| _version_ | 1818617246315970560 |
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| author | Marco Ortu Giuseppe Destefanis Daniel Graziotin Michele Marchesi Roberto Tonelli |
| author_facet | Marco Ortu Giuseppe Destefanis Daniel Graziotin Michele Marchesi Roberto Tonelli |
| author_sort | Marco Ortu |
| collection | DOAJ |
| description | Software engineering methodologies rely on version control systems such as git to store source code artifacts and manage changes to the codebase. Pull requests include chunks of source code, history of changes, log messages around a proposed change of the mainstream codebase, and much discussion on whether to integrate such changes or not. A better understanding of what contributes to a pull request fate and latency will allow us to build predictive models of what is going to happen and when. Several factors can influence the acceptance of pull requests, many of which are related to the individual aspects of software developers. In this study, we aim to understand how the affect (e.g., sentiment, discrete emotions, and valence-arousal-dominance dimensions) expressed in the discussion of pull request issues influence the acceptance of pull requests. We conducted a mining study of large git software repositories and analyzed more than 150,000 issues with more than 1,000,000 comments in them. We built a model to understand whether the affect and the politeness have an impact on the chance of issues and pull requests to be merged-i.e., the code which fixes the issue is integrated in the codebase. We built two logistic classifiers, one without affect metrics and one with them. By comparing the two classifiers, we show that the affect metrics improve the prediction performance. Our results show that valence (expressed in comments received and posted by a reporter) and joy expressed in the comments written by a reporter are linked to a higher likelihood of issues to be merged. On the contrary, sadness, anger, and arousal expressed in the comments written by a reporter, and anger, arousal, and dominance expressed in the comments received by a reporter, are linked to a lower likelihood of a pull request to be merged. |
| first_indexed | 2024-12-16T17:02:39Z |
| format | Article |
| id | doaj.art-07cfb3b47c8442049f9f96669b2c4d4d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:02:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-07cfb3b47c8442049f9f96669b2c4d4d2022-12-21T22:23:41ZengIEEEIEEE Access2169-35362020-01-01811089711090710.1109/ACCESS.2020.30026639117137How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHubMarco Ortu0Giuseppe Destefanis1https://orcid.org/0000-0003-3982-6355Daniel Graziotin2Michele Marchesi3Roberto Tonelli4Dipartimento di Ingegneria Elettrica ed Elettronica, University of Cagliari, Cagliari, ItalyDepartment of Computer Science, Brunel University London, Uxbridge, U.KInstitute of Software Technology, University of Stuttgart, Stuttgart, GermanyDipartimento di Matematica e Informatica, University of Cagliari, Cagliari, ItalyDipartimento di Matematica e Informatica, University of Cagliari, Cagliari, ItalySoftware engineering methodologies rely on version control systems such as git to store source code artifacts and manage changes to the codebase. Pull requests include chunks of source code, history of changes, log messages around a proposed change of the mainstream codebase, and much discussion on whether to integrate such changes or not. A better understanding of what contributes to a pull request fate and latency will allow us to build predictive models of what is going to happen and when. Several factors can influence the acceptance of pull requests, many of which are related to the individual aspects of software developers. In this study, we aim to understand how the affect (e.g., sentiment, discrete emotions, and valence-arousal-dominance dimensions) expressed in the discussion of pull request issues influence the acceptance of pull requests. We conducted a mining study of large git software repositories and analyzed more than 150,000 issues with more than 1,000,000 comments in them. We built a model to understand whether the affect and the politeness have an impact on the chance of issues and pull requests to be merged-i.e., the code which fixes the issue is integrated in the codebase. We built two logistic classifiers, one without affect metrics and one with them. By comparing the two classifiers, we show that the affect metrics improve the prediction performance. Our results show that valence (expressed in comments received and posted by a reporter) and joy expressed in the comments written by a reporter are linked to a higher likelihood of issues to be merged. On the contrary, sadness, anger, and arousal expressed in the comments written by a reporter, and anger, arousal, and dominance expressed in the comments received by a reporter, are linked to a lower likelihood of a pull request to be merged.https://ieeexplore.ieee.org/document/9117137/Software engineeringbehavioral software engineeringhuman aspectssentiment analysissoftware qualityversion control systems |
| spellingShingle | Marco Ortu Giuseppe Destefanis Daniel Graziotin Michele Marchesi Roberto Tonelli How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub IEEE Access Software engineering behavioral software engineering human aspects sentiment analysis software quality version control systems |
| title | How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub |
| title_full | How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub |
| title_fullStr | How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub |
| title_full_unstemmed | How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub |
| title_short | How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub |
| title_sort | how do you propose your code changes empirical analysis of affect metrics of pull requests on github |
| topic | Software engineering behavioral software engineering human aspects sentiment analysis software quality version control systems |
| url | https://ieeexplore.ieee.org/document/9117137/ |
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