Conceptual process models and quantitative analysis of classification problems in Scrum software development practices

Abstract

We

propose a novel classification method that integrates into existing

agile software development practices by collecting data records

generated by software and tools used in the development process. We extract features from the collected data and create visualizations that provide insights, and feed the data into a prediction framework consisting of a deep neural network. The features and results are validated against conceptual frameworks that model the development methodologies as similar processes in other contexts. Initial results show that the visualization and prediction techniques provide promising outcomes that may help development teams and management gain better understanding of past events and future risks.