Exploring Retrospective Meeting Practices and the Use of Data in Agile Teams

Authors: Alessandra Maciel Paz Milani, Margaret-Anne Storey, Vivek Katial, Lauren Peate

License: CC BY-SA 4.0

Abstract: Retrospectives are vital for software development teams to continuously enhance their processes and teamwork. Despite the increasing availability of objective data generated throughout the project and software development processes, many teams do not fully utilize this information in retrospective meetings. Instead, they often rely on subjective data, anecdotal insights and their memory. While some literature underscores the value of data-driven retrospectives, little attention has been given to the role data can play and the challenges of effectively incorporating objective project data into these meetings. To address this gap, we conducted a survey with 19 practitioners on retrospective meeting practices and how their teams gather and use subjective and objective data in their retrospectives. Our findings confirm that although teams routinely collect project data, they seldom employ it systematically during retrospectives. Furthermore, this study provides insights into retrospective practices by exploring barriers to project data utilization, including psychological safety concerns and the disconnect between data collection and meaningful integration of data into retrospective meetings. We close by considering preliminary insights that may help to mitigate these concerns and how future research might build on our paper findings to support the integration of project data into retrospective meetings, fostering a balance between human-centric reflections and data-driven insights.

Submitted to arXiv on 05 Feb. 2025

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.