research-article

Team discussions and dynamics during DevOps tool adoptions in OSS projects

Authors:

Vladimir FilkovAuthors Info & Claims

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

Pages 697 - 708

https://doi.org/10.1145/3324884.3416640

Published: 27 January 2021 Publication History

Abstract

In Open Source Software (OSS) projects, pre-built tools dominate DevOps-oriented pipelines. In practice, a multitude of configuration management, cloud-based continuous integration, and automated deployment tools exist, and often more than one for each task. Tools are adopted (and given up) by OSS projects regularly. Prior work has shown that some tool adoptions are preceded by discussions, and that tool adoptions can result in benefits to the project. But important questions remain: how do teams decide to adopt a tool? What is discussed before the adoption and for how long? And, what team characteristics are determinant of the adoption?

In this paper, we employ a large-scale empirical study in order to characterize the team discussions and to discern the teamlevel determinants of tool adoption into OSS projects' development pipelines. Guided by theories of team and individual motivations and dynamics, we perform exploratory data analyses, do deep-dive case studies, and develop regression models to learn the determinants of adoption and discussion length, and the direction of their effect on the adoption. From data of commit and comment traces of large-scale GitHub projects, our models find that prior exposure to a tool and member involvement are positively associated with the tool adoption, while longer discussions and the number of newer team members associate negatively. These results can provide guidance beyond the technical appropriateness for the timeliness of tool adoptions in diverse programmer teams.

Our data and code is available at https://github.com/lkyin/tool_adoptions.

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Cited By

Jiang WYasmin JJones JSynovic NKuo JBielanski NTian YThiruvathukal GDavis JSpinellis DConstantinou EBacchelli A(2024)PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source SoftwareProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644907(431-443)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644907
Amaro RPereira Rda Silva M(2023)Capabilities and Practices in DevOps: A Multivocal Literature ReviewIEEE Transactions on Software Engineering10.1109/TSE.2022.316662649:2(883-901)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TSE.2022.3166626
Gu HHe HZhou M(2023)Self-Admitted Library Migrations in Java, JavaScript, and Python Packaging Ecosystems: A Comparative Study2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER56733.2023.00064(627-638)Online publication date: Mar-2023
https://doi.org/10.1109/SANER56733.2023.00064
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cover image ACM Conferences

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

December 2020

1449 pages

ISBN:9781450367684

DOI:10.1145/3324884

General Chair:
John Grundy,
Program Chairs:
Claire Le Goues,
David Lo

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 January 2021

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ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering

December 21 - 25, 2020

Virtual Event, Australia

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Cited By

Jiang WYasmin JJones JSynovic NKuo JBielanski NTian YThiruvathukal GDavis JSpinellis DConstantinou EBacchelli A(2024)PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source SoftwareProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644907(431-443)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644907
Amaro RPereira Rda Silva M(2023)Capabilities and Practices in DevOps: A Multivocal Literature ReviewIEEE Transactions on Software Engineering10.1109/TSE.2022.316662649:2(883-901)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TSE.2022.3166626
Gu HHe HZhou M(2023)Self-Admitted Library Migrations in Java, JavaScript, and Python Packaging Ecosystems: A Comparative Study2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER56733.2023.00064(627-638)Online publication date: Mar-2023
https://doi.org/10.1109/SANER56733.2023.00064
He HHe RGu HZhou MSpinellis DGousios GChechik MDi Penta M(2021)A large-scale empirical study on Java library migrations: prevalence, trends, and rationalesProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3468571(478-490)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3468571

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