GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study

TL;DR

This paper presents an empirical study on using a GenAI-powered tool to automate backlog grooming in Agile projects, significantly improving efficiency and accuracy.

Contribution

It introduces a novel Jira plug-in that leverages GenAI and vector databases for automated backlog management, demonstrating practical effectiveness.

Findings

Achieved 100% precision in backlog tasks detection

Reduced backlog grooming time by 45%

Enhanced user experience in backlog management

Abstract

Effective backlog management is critical for ensuring that development teams remain aligned with evolving requirements and stakeholder expectations. However, as product backlogs consistently grow in scale and complexity, they tend to become cluttered with redundant, outdated, or poorly defined tasks, complicating prioritization and decision making processes. This study investigates whether a generative-AI (GenAI) assistant can automate backlog grooming in Agile software projects without sacrificing accuracy or transparency. Through Design Science cycles, we developed a Jira plug-in that embeds backlog issues with the vector database, detects duplicates via cosine similarity, and leverage the GPT-4o model to propose merges, deletions, or new issues. We found that AI-assisted backlog grooming achieved 100 percent precision while reducing the time-to-completion by 45 percent. The findings demonstrated the tool's potential to streamline backlog refinement processes while improving user experiences.