A Dataset of Microservices-based Open-Source Projects
D'Aragona, Dario Amoroso; Bakhtin, Alexander; Li, Xiaozhou; Su, Ruoyu; Adams, Lauren; Aponte, Ernesto; Boyle, Francis; Boyle, Patrick; Koerner, Rachel; Lee, Joseph; Tian, Fangchao; Wang, Yuqing; Nyyssola, Jesse; Quevedo, Ernesto; Rahaman, Shahidur Md; Abdelfattah, Amr S.; Mantyla, Mika; Cerny, Tomas; Taibi, Davide (2024-07-02)
ACM
02.07.2024
Amoroso d’Aragona, D., Bakhtin, A., Li, X., Su, R., Adams, L., Aponte, E., Boyle, F., Boyle, P., Koerner, R., Lee, J., Tian, F., Wang, Y., Nyyssölä, J., Quevedo, E., Rahaman, S. M., Abdelfattah, A. S., Mäntylä, M., Cerny, T., & Taibi, D. (2024). A dataset of microservices-based open-source projects. Proceedings of the 21st International Conference on Mining Software Repositories, 504–509. https://doi.org/10.1145/3643991.3644890
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work licensed under Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work licensed under Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202408165443
https://urn.fi/URN:NBN:fi:oulu-202408165443
Tiivistelmä
Abstract
Researchers in the microservices community often resort to demonstrating the impact of their proposed advancements on custom-made microservices projects. This is a possible source of bias that can reduce the trustworthiness of the results. Moreover, it is hard to compare advances in small projects, often developed due to lack of time. It is common across disciplines to recognize benchmarks that mitigate bias and unify the advancements' impact. To facilitate the identification of available open-source microservice projects (OSS-MS), we performed a comprehensive study to identify, curate, and catalog OSS-MS. We started with 389559 projects and filtered them down to 3804 projects that we manually labeled. After manual labeling, our dataset contains 378 projects with three or more microservices and with over 100 commits. We document the projects from many perspectives, including project size, platform, number of contributors, project purpose, and foundation support. This dataset can serve researchers as a roadmap to identify benchmarks, as our dataset can be used to answer questions such as whether the number of services impacts the issue count.
Researchers in the microservices community often resort to demonstrating the impact of their proposed advancements on custom-made microservices projects. This is a possible source of bias that can reduce the trustworthiness of the results. Moreover, it is hard to compare advances in small projects, often developed due to lack of time. It is common across disciplines to recognize benchmarks that mitigate bias and unify the advancements' impact. To facilitate the identification of available open-source microservice projects (OSS-MS), we performed a comprehensive study to identify, curate, and catalog OSS-MS. We started with 389559 projects and filtered them down to 3804 projects that we manually labeled. After manual labeling, our dataset contains 378 projects with three or more microservices and with over 100 commits. We document the projects from many perspectives, including project size, platform, number of contributors, project purpose, and foundation support. This dataset can serve researchers as a roadmap to identify benchmarks, as our dataset can be used to answer questions such as whether the number of services impacts the issue count.
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