Exploring the Role of Generative AI for Improving and Optimizing Sprint Planning in Agile Development

Abstract

Generative AI is transforming how software teams approach project management. This thesis investigates the application of large language models, such as ChatGPT, in Agile sprint planning. It begins with a review of current research and identifies persistent challenges in traditional sprint planning, including subjective backlog selection, inconsistent story point estimation, and uneven task assignment.

To address these issues, a web-based tool called GenSP was developed. GenSP leverages GenAI APIs to generate sprint backlogs, estimate story points, break down user stories, and assign tasks. The tool was evaluated using real project data, and its effectiveness was further assessed through a survey of experienced Agile practitioners.

The results indicate that GenAI can enhance sprint planning by automating backlog refinement, story point estimation, and task breakdown. However, the study also highlights concerns regarding the need for human oversight, the handling of complex business logic, and the protection of sensitive data.

In summary, this research demonstrates that GenAI can make sprint planning more effective and efficient. The findings provide practical guidance for teams considering AI integration in Agile workflows and suggest directions for future research and development.