The Impact of Code Ownership of DevOps Artefacts on the Outcome of DevOps CI Builds

被引:0
作者
Kola-Olawuyi, Ajiromola [1 ]
Weeraddana, Nimmi Rashinika [1 ]
Nagappan, Meiyappan [1 ]
机构
[1] Univ Waterloo, Waterloo, ON, Canada
来源
2024 IEEE/ACM 21ST INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES, MSR | 2024年
关键词
DevOps; CodeOwnership; Continuous Integrations; Empirical Study; CONTINUOUS INTEGRATION;
D O I
10.1145/3643991.3644924
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
DevOps is a key element in sustaining the quality and efficiency of software development. Yet, the effectiveness of DevOps methodologies extends beyond just technological expertise. It is greatly affected by the manner in which teams handle and engage with DevOps artefacts. Grasping the intricacies of code ownership and contribution patterns within DevOps artefacts is vital for refining strategies and ensuring they deliver their full potential. There are two main strategies to manage DevOps artefacts as suggested in prior work: (1) all project developers need to contribute to DevOps artefacts, and (2) a dedicated group of developers needs to be authoring DevOps artefacts. To analyze which strategy works best for Open-Source Software (OSS) projects, we conduct an empirical analysis on a dataset of 892,193 CircleCI builds spanning 1,689 OSS projects. We employ a two-pronged approach to our study. First, we investigate the impact of chronological code ownership of DevOps artefacts on the outcome of a CI build on a build level. Second, we study the impact of the Skewness of DevOps contributions on the success rate of CI builds at the project level. Our findings reveal that, in general, larger chronological ownership and higher Skewness values of DevOps contributions are related to more successful build outcomes and higher rates of successful build outcomes, respectively. We further find that projects with low Skewness values could have high build success rates when the number of developers in the project is relatively small. Thus, our results suggest that while larger software organizations are better off having dedicated DevOps developers, smaller organizations would benefit from having all developers involved in DevOps.
引用
收藏
页码:543 / 555
页数:13
相关论文
共 71 条
[1]   A Machine Learning Approach to Improve the Detection of CI Skip Commits [J].
Abdalkareem, Rabe ;
Mujahid, Suhaib ;
Shihab, Emad .
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2021, 47 (12) :2740-2754
[2]   Which Commits Can Be CI Skipped? [J].
Abdalkareem, Rabe ;
Mujahid, Suhaib ;
Shihab, Emad ;
Rilling, Juergen .
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2021, 47 (03) :448-463
[3]   Studying the characteristics of AIOps projects on GitHub [J].
Aghili, Roozbeh ;
Li, Heng ;
Khomh, Foutse .
EMPIRICAL SOFTWARE ENGINEERING, 2023, 28 (06)
[4]   NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].
AKAIKE, H .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) :716-723
[5]  
Akinwande MO, 2015, Open Journal of Statistics, V05, P754, DOI [10.4236/ojs.2015.57075, 10.4236/ojs.2015.57075, DOI 10.4236/OJS.2015.57075]
[6]   Code Review Practices for Refactoring Changes: An Empirical Study on OpenStack [J].
AlOmar, Eman Abdullah ;
Chouchen, Moataz ;
Mkaouer, Mohamed Wiem ;
Ouni, Ali .
2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), 2022, :689-701
[7]   The Promises and Perils of Mining Git [J].
Bird, Christian ;
Rigby, Peter C. ;
Barr, Earl T. ;
Hamilton, David J. ;
German, Daniel M. ;
Devanbu, Prem .
2009 6TH IEEE INTERNATIONAL WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES, 2009, :1-+
[8]  
Bird Christian., 2011, Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, Szeged, Hungary, September 2011, P4
[9]   Generalized linear mixed models: a practical guide for ecology and evolution [J].
Bolker, Benjamin M. ;
Brooks, Mollie E. ;
Clark, Connie J. ;
Geange, Shane W. ;
Poulsen, John R. ;
Stevens, M. Henry H. ;
White, Jada-Simone S. .
TRENDS IN ECOLOGY & EVOLUTION, 2009, 24 (03) :127-135
[10]   BUILDFAST: History-Aware Build Outcome Prediction for Fast Feedback and Reduced Cost in Continuous Integration [J].
Chen, Bihuan ;
Chen, Linlin ;
Zhang, Chen ;
Peng, Xin .
2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020), 2020, :42-53