Sentiment analysis of commit comments in GitHub: an empirical study

Guzmán, Emitzá; Azócar, David; Li, Yang

doi:10.1145/2597073.2597118

Cited by 215 publications

(127 citation statements)

References 4 publications

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“…Their research identified relationships between positive and negative sentiment and factors such as the programming language used, team members' geographic locations, day of the week and project approval [14]. Like Guzman et al we apply sentiment analysis to the messages left behind during the development process, but focused our approach on the comparisons of projects using TDD process with those not using TDD process.…”

Section: B Sentiment In Software Artifactsmentioning

confidence: 99%

Analysis of Test Driven Development on sentiment and coding activities in GitHub repositories

Borle¹,

Feghhi²,

Hindle³

2016

Preprint

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Abstract-Testing is an integral part of the softwaredevelopment lifecycle, approached with varying degrees of rigor by different process models. Agile process models advocate Test Driven Development (TDD) as one among their key practices for reducing costs and improving code quality. In this paper we comparatively analyze GitHub repositories that adopt TDD against repositories that do not, in order to determine how TDD affects a number of variables related to productivity and developer satisfaction, two aspects that should be considered in a cost-benefit analysis of the paradigm.In this study, we searched through GitHub and found that a relatively small subset of Java-based repositories can be seen to adopt TDD, and an even smaller subset can be confidently identified as rigorously adhering to TDD. For comparison purposes, we created two same-size control sets of repositories. We then compared the repositories in these two sets in terms of number of test files, average commit velocity, number of commits that reference bugs, number of issues recorded, whether they use continuous integration, and the sentiment of their developers' commits. We found some interesting and significant differences between the two sets, including higher commit velocity and increased likelihood of continuous integration for TDD repositories.

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Section: B Sentiment In Software Artifactsmentioning

confidence: 99%

Analysis of Test Driven Development on sentiment and coding activities in GitHub repositories

Borle¹,

Feghhi²,

Hindle³

2016

Preprint

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“…This three metrics have been used by other researchers, i.e., politeness [23] and [24], sentiment [25] and [26], and emotion [9].…”

Section: Introductionmentioning

confidence: 99%

Are Bullies More Productive? Empirical Study of Affectiveness vs. Issue Fixing Time

Ortu

Adams

Destefanis

et al. 2015

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories

126

159

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Abstract-Human Affectiveness, i.e., the emotional state of a person, plays a crucial role in many domains where it can make or break a team's ability to produce successful products. Software development is a collaborative activity as well, yet there is little information on how affectiveness impacts software productivity. As a first measure of this impact, this paper analyzes the relation between sentiment, emotions and politeness of developers in more than 560K Jira comments with the time to fix a Jira issue. We found that the happier developers are (expressing emotions such as JOY and LOVE in their comments), the shorter the issue fixing time is likely to be. In contrast, negative emotions such as SADNESS, are linked with longer issue fixing time. Politeness plays a more complex role and we empirically analyze its impact on developers' productivity.

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“…Sentiment polarity analysis has been recently applied in the software engineering context to study commit comments in GitHub [9], GitHub discussions related to security [10], productivity in Jira issue resolution [11], activity of contributors in Gentoo [12] and evolution of developers' sentiments in the openSUSE Factory [13]. It has also been suggested when assessing technical candidates on the social web [14].…”

Section: Introductionmentioning

confidence: 99%

Choosing your weapons: On sentiment analysis tools for software engineering research

Jongeling

Datta

Serebrenik

2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Abstract-Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain.In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and STACK OVERFLOW questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to contradictory conclusions.

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Sentiment analysis of commit comments in GitHub: an empirical study

Cited by 215 publications

References 4 publications

Analysis of Test Driven Development on sentiment and coding activities in GitHub repositories

Analysis of Test Driven Development on sentiment and coding activities in GitHub repositories

Are Bullies More Productive? Empirical Study of Affectiveness vs. Issue Fixing Time

Choosing your weapons: On sentiment analysis tools for software engineering research

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