Impacts of Generative AI on Agile Teams' Productivity: A Multi-Case Longitudinal Study
Rafael Tomaz, Paloma Guenes, Allysson Allex Ara\'ujo, Maria Teresa Baldassarre, Marcos Kalinowski

TL;DR
This longitudinal study shows that Generative AI tools significantly enhance agile teams' productivity and well-being by increasing performance and efficiency without increasing activity levels, highlighting the importance of multi-dimensional evaluation frameworks.
Contribution
It provides the first long-term, multi-case analysis of GenAI's impact on team productivity in industrial agile settings using the SPACE framework.
Findings
GenAI improves team performance and well-being.
Performance and perceived efficiency increase sharply.
Developer activity remains flat despite productivity gains.
Abstract
Context: Generative Artificial Intelligence (GenAI) tools, such as GitHub Copilot and GPT tools, represent a paradigm shift in software engineering. While their impact is clear, most studies are short-term, focused on individual experiments. The sustained, team-level effects on productivity within industrial agile environments remain largely uncharacterized. Goal: This study aims to provide a longitudinal evaluation of GenAI's impact on agile software teams. We characterize its effect on developers' productivity by applying the multi-dimensional SPACE framework. Method: We conducted a multi-case longitudinal study involving 3 agile teams at a large technology consulting firm for around 13 months. We collected and compared quantitative telemetry (Jira, SonarQube, Git) and qualitative survey data from historical (pre-adoption) and research (post-adoption) sprints. Conclusion: GenAI tools…
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Taxonomy
TopicsSoftware Engineering Techniques and Practices · Software Engineering Research · Team Dynamics and Performance
