Analyzing DevOps Practices Through Merge Request Data: A Case Study in Networking Software Company

• URL: http://arxiv.org/abs/2503.14677v1
• Date: Tue, 18 Mar 2025 19:33:34 GMT
• Title: Analyzing DevOps Practices Through Merge Request Data: A Case Study in Networking Software Company
• Authors: Samah Kansab, Matthieu Hanania, Francis Bordeleau, Ali Tizghadam,
• Abstract summary: GitLab's Request (MR) mechanism streamlines code submission and review.<n>MR data reflects broader aspects, including collaboration patterns, productivity, and process optimization.<n>This study examines 26.7k MRs from four teams across 116 projects of a networking software company.
• Score: 2.5999037208435705
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: DevOps integrates collaboration, automation, and continuous improvement, enhancing agility, reducing time to market, and ensuring consistent software releases. A key component of this process is GitLab's Merge Request (MR) mechanism, which streamlines code submission and review. Studies have extensively analyzed MR data and similar mechanisms like GitHub pull requests and Gerrit Code Review, focusing on metrics such as review completion time and time to first comment. However, MR data also reflects broader aspects, including collaboration patterns, productivity, and process optimization. This study examines 26.7k MRs from four teams across 116 projects of a networking software company to analyze DevOps processes. We first assess the impact of external factors like COVID-19 and internal changes such as migration to OpenShift. Findings show increased effort and longer MR review times during the pandemic, with stable productivity and a lasting shift to out-of-hours work, reaching 70% of weekly activities. The transition to OpenShift was successful, with stabilized metrics over time. Additionally, we identify prioritization patterns in branch management, particularly in stable branches for new releases, underscoring the importance of workflow efficiency. In code review, while bots accelerate review initiation, human reviewers remain crucial in reducing review completion time. Other factors, such as commit count and reviewer experience, also influence review efficiency. This research provides actionable insights for practitioners, demonstrating how MR data can enhance productivity, effort analysis, and overall efficiency in DevOps.

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