An empirical study on performance comparisons of different types of DevOps team formations

Introduction Despite all the efforts to successfully implement DevOps practices, principles, and cultural change, there is still a lack of understanding on how DevOps team structure formation and performance differences are related. The lack of a ground truth for DevOps team structure formation and performance has become a persistent and relevant problem for companies and researchers. Methods In this study, we propose a framework for DevOps team Formation–Performance and conduct a survey to examine the relationships between team formations and performance with the five metrics we identified, two of which are novel. We conducted an empirical study using a survey to gather data. We employed targeted outreach on a social media platform along via a snowball sampling and sent 380 messages to DevOps professionals worldwide. This approach resulted in 122 positive responses and 105 completed surveys, achieving a 69.7% response rate from those who agreed to participate. Results The research shows that implementing the DevOps methodology enhances team efficiency across various team structures, with the sole exception of “Separate Development and Operation teams with limited collaboration”. Moreover, the study reveals that all teams experienced improvements in Repair/Recovery performance metric following DevOps adoption. Notably, the “Separate Development and Operation teams with high collaboration” formation emerged as the top performer in the key metrics, including Deployment Frequency, Number of Incidents, and Number of Failures/Service Interruptions. The analysis further indicates that different DevOps organizational formations do not significantly impact Lead Time, Repair/Recovery, and Number of Failures/Service Interruptions in terms of goal achievement. However, a statistically significant disparity was observed between “Separate Development and Operation teams with high collaboration” and “A single team formation” regarding the Deployment Frequency goal achievement percentage. Discussion The analysis confirms that DevOps adoption improves performance across most team formations, with the exception of “Separate Development and Operation teams with limited collaboration” (TeamType1), which shows significant improvement only in Mean Time to Recovery (MTTR). Standardized effect size calculations (Cohen’s d) reveal that TeamType2 (“Separate Development and Operation teams with high collaboration”) consistently achieves large effects in Deployment Frequency (DF), Number of Incidents (NoI), and Number of Failures/Service Interruptions (NoF/NoSI), while TeamType3 shows strong results for Lead Time (LT) and NoF/NoSI. MTTR improvements are large across all formations, with TeamType4 performing best in this metric. These findings suggest that collaboration intensity is a critical determinant of performance gains. While team formation type does not significantly influence LT, MTTR, or NoF/NoSI goal achievement, DF goal achievement is significantly higher for TeamType2 compared to TeamType4, highlighting the potential competitive advantage of high-collaboration structures.

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Publication Details

Journal
Frontiers in Computer Science
Published
2025-09-01
DOI
https://doi.org/10.3389/fcomp.2025.1554299
Citations
1
Primary Topic
Software Engineering Techniques and Practices
Type
article
Field-Weighted Citation Impact
3.25
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article

An empirical study on performance comparisons of different types of DevOps team formations

Mehmet N. Aydın, Halil Ergun Korkmaz
1 citations
Frontiers in Computer Science
Software Engineering Techniques and Practices
3.25
article

An empirical study on performance comparisons of different types of DevOps team formations

Mehmet N. Aydın, Halil Ergun Korkmaz
article en
1 citations

Abstract

Introduction Despite all the efforts to successfully implement DevOps practices, principles, and cultural change, there is still a lack of understanding on how DevOps team structure formation and performance differences are related. The lack of a ground truth for DevOps team structure formation and performance has become a persistent and relevant problem for companies and researchers. Methods In this study, we propose a framework for DevOps team Formation–Performance and conduct a survey to examine the relationships between team formations and performance with the five metrics we identified, two of which are novel. We conducted an empirical study using a survey to gather data. We employed targeted outreach on a social media platform along via a snowball sampling and sent 380 messages to DevOps professionals worldwide. This approach resulted in 122 positive responses and 105 completed surveys, achieving a 69.7% response rate from those who agreed to participate. Results The research shows that implementing the DevOps methodology enhances team efficiency across various team structures, with the sole exception of “Separate Development and Operation teams with limited collaboration”. Moreover, the study reveals that all teams experienced improvements in Repair/Recovery performance metric following DevOps adoption. Notably, the “Separate Development and Operation teams with high collaboration” formation emerged as the top performer in the key metrics, including Deployment Frequency, Number of Incidents, and Number of Failures/Service Interruptions. The analysis further indicates that different DevOps organizational formations do not significantly impact Lead Time, Repair/Recovery, and Number of Failures/Service Interruptions in terms of goal achievement. However, a statistically significant disparity was observed between “Separate Development and Operation teams with high collaboration” and “A single team formation” regarding the Deployment Frequency goal achievement percentage. Discussion The analysis confirms that DevOps adoption improves performance across most team formations, with the exception of “Separate Development and Operation teams with limited collaboration” (TeamType1), which shows significant improvement only in Mean Time to Recovery (MTTR). Standardized effect size calculations (Cohen’s d) reveal that TeamType2 (“Separate Development and Operation teams with high collaboration”) consistently achieves large effects in Deployment Frequency (DF), Number of Incidents (NoI), and Number of Failures/Service Interruptions (NoF/NoSI), while TeamType3 shows strong results for Lead Time (LT) and NoF/NoSI. MTTR improvements are large across all formations, with TeamType4 performing best in this metric. These findings suggest that collaboration intensity is a critical determinant of performance gains. While team formation type does not significantly influence LT, MTTR, or NoF/NoSI goal achievement, DF goal achievement is significantly higher for TeamType2 compared to TeamType4, highlighting the potential competitive advantage of high-collaboration structures.

Frontiers in Computer ScienceVol. 7
Kadir Has University (TR), Boğaziçi University (TR)
Openalex Percentile: Top 7%
Software Engineering Techniques and Practices
3.25
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