Sentiment Analysis of Travis CI Builds

Souza, Rodrigo Fernandes de; Silva, Bruno

doi:10.1109/msr.2017.27

Cited by 20 publications

(19 citation statements)

References 9 publications

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“…By leveraging this data, researchers can use coverage data without having to collect and run historical versions of software, which may be very difficult due to missing dependencies and other infrastucture-related problems. We expect that analysis of data from Coveralls can lead to new insights similar to how analyses of GitHub and TravisCI [13,16,33,37,38,42,44] data enabled researchers to obtain new insights into software development.…”

Section: Discussionmentioning

confidence: 99%

A large-scale study of test coverage evolution

Hilton

Bell

Marinov

2018

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

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Statement coverage is commonly used as a measure of test suite quality. Coverage is often used as a part of a code review process: if a patch decreases overall coverage, or is itself not covered, then the patch is scrutinized more closely. Traditional studies of how coverage changes with code evolution have examined the overall coverage of the entire program, and more recent work directly examines the coverage of patches (changed statements). We present an evaluation much larger than prior studies and moreover consider a new, important kind of change-coverage changes of unchanged statements. We present a large-scale evaluation of code coverage evolution over 7,816 builds of 47 projects written in popular languages including Java, Python, and Scala. We find that in large, mature projects, simply measuring the change to statement coverage does not capture the nuances of code evolution. Going beyond considering statement coverage as a simple ratio, we examine how the set of statements covered evolves between project revisions. We present and study new ways to assess the impact of a patch on a project's test suite quality that both separates coverage of the patch from coverage of the non-patch, and separates changes in coverage from changes in the set of statements covered. CCS CONCEPTS • Software and its engineering → Software testing and debugging;

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Section: Discussionmentioning

confidence: 99%

A large-scale study of test coverage evolution

Hilton

Bell

Marinov

2018

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

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“…Also more significant commits message express more sentiment than small commit messages, weekdays and hours of that day did not show any significant relationship with developer sentiment. Souza and Silva [14] studied the sentiment related to building status, evaluated 1,262 projects from GitHub, and more than 609k builds, they found that the commit message following broken builds has a week correlation with negative sentiment.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, researchers have published several papers, regarding developer sentiment analysis, on GitHub interactions [5,7,8,9,12,14]. However, what causes positive or negative variances in the developers sentiment, as well as the duration of the variation, is not entirely understood yet; many external influencing factors remain unexplored.…”

Section: Introductionmentioning

confidence: 99%

“…Their results showed that the SentiStrength tool [16] presented the best results in most of the datasets. Additionally, the previous work [7,8,14] have successfully used SentiStrength [16] to perform sentiment analysis of software engineering interactions, such as commits, issues, and pull requests. Therefore, in this paper, we decided to use SentiStrength to perform the sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally 'static' is a common term in some programming languages such as Java. Based on the Novielli et al [10] suggestion, and in the previous work [14,8], we decided to change the default SentiStrength dictionary to evaluate our data set better.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing The Impact Of Feedback In GitHub On The Software Developer's Mood

Santos

Caetano

Oliveira

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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Software development depends on cooperation between people, and the way it works can define the future of the software project. Developers emotions affect their productivity and way they work, yet there is little information about how developers can influence the mood of each other. As a first step toward understanding how feedback may affect the developers' sentiment, this paper analyzes the mood variations on more than 78k pull requests and 268k pull comments on GitHub. We found that in 31.16% of the cases the developers presented a significant mood variation within one hour when receiving feedback on their pull requests. The variation reduces to 18.16% when evaluating one day before and after the commentary. In software projects with less than 34k lines of code, the number of developers that never contribute again after receiving a negative comment on the first pull request is 10.97%; this number more than doubles to 24.02% when evaluating projects with more than 197k lines of code.

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