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TravisTorrent: Synthesizing Travis CI and GitHub for Full-Stack Research on Continuous Integration

Open access
Beller, Moritz, Gousios, Georgios, Zaidman, Andy·2017·2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR)

Abstract

Continuous Integration (CI) has become a best practice of modern software development. Thanks in part to its tight integration with GitHub, Travis CI has emerged as arguably the most widely used CI platform for Open-Source Software (OSS) development. However, despite its prominent role in Software Engineering in practice, the benefits, costs, and implications of doing CI are all but clear from an academic standpoint. Little research has been done, and even less was of quantitative nature. In order to lay the groundwork for data-driven research on CI, we built TravisTorrent, travistorrent.testroots.org, a freely available data set based on Travis CI and GitHub that provides easy access to hundreds of thousands of analyzed builds from more than 1,000 projects. Unique to TravisTorrent is that each of its 2,640,825 Travis builds is synthesized with meta data from Travis CI's API, the results of analyzing its textual build log, a link to the GitHub commit which triggered the build, and dynamically aggregated project data from the time of commit extracted through GHTorrent.

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Metadata

Authors
Beller, M., Gousios, G., & Zaidman, A.
Venue
2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR)
Publisher
IEEE
Published
2017-05
Pages
447-450
OpenAlex
W2703757306
Cited by
190
References
0
FWCI
21.60
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: TravisTorrent: Synthesizing Travis CI and GitHub for Full-Stack Research on Continuous Integration. https://app.cochat.ai/papers/10.1109/msr.2017.24
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Beller, M., Gousios, G., & Zaidman, A. (2017). TravisTorrent: Synthesizing Travis CI and GitHub for Full-Stack Research on Continuous Integration. 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR). https://doi.org/10.1109/msr.2017.24

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.