Exploring issues of story-based effort estimation in Agile Software Development (ASD)

Context

Effort estimation based on user stories plays a pivotal role in agile software development, where accurate predictions of project efforts are vital for success. While various supervised ML tools attempt to estimate effort, the prevalence of estimation errors presents significant challenges, as evidenced by the CHAOS report by the Standish Group, which highlights incorrect estimations contributing to a substantial percentage of failed agile projects.

Objectives

This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy of the user story-based estimation method.

Methods

To achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. The review encompasses diverse estimation methods, user story attributes, and the array of challenges that can result from inaccurate estimations.

Results

The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition as crucial determinants of estimation reliability. Consistency in user stories, technical complexities, and task engineering practices also emerge as significant contributors to estimation inaccuracies. The study underscores the interconnectedness of these issues, emphasizing the need for a standardized protocol to minimize inaccuracies and enhance estimation precision.

Conclusion

In light of the findings, it becomes evident that addressing the multi-dimensional factors influencing user story-based effort estimation is imperative for successful agile software development. The study underscores the interplay of various aspects, such as team dynamics, task complexity, and requirement engineering, in achieving accurate estimations. By recognizing these challenges and implementing recommended solutions, software development processes can avoid failures and enhance their prospects of success in the agile paradigm.