Applying Software Design Metrics to Developer Story: A Supervised Machine Learning Analysis

Abstract

Object-oriented analysis is a significant step that plays a vital role in the success of software development. The planning and management stages, in particular, profoundly rely on the deliverance of an accurate estimate that takes the software's complexity and size into consideration. Today, several software industries are transforming their development methodologies to Agile due to its ability to deliver value in a short time and its cost efficiency. However, Agile methods prevent heavyweight modeling analysis and depend on user stories to drive the estimation process. Because user stories are descriptive language, they may not provide a clear picture for the implementation. Also, they may not help Agile developers give an accurate estimation due to their difficulty in measuring the complexity and size of a feature. Thus, this paper presents a new Agile artifact called developer story that allows the Agile developer to not only analyze and design software products but also predict the size of each feature, including its complexity. In this paper, a case study is presented that shows how the utilization of developer story is a practical approach in predicting the source code size of a feature and its complexity.