Sprint2Vec: a deep characterization of sprints in iterative software development

Iterative approaches like Agile Scrum are commonly adopted to enhance the software development process. However,
challenges such as schedule and budget overruns still persist in many software projects. Several approaches employ machine learning
techniques, particularly classification, to facilitate decision-making in iterative software development. Existing approaches often
concentrate on characterizing a sprint to predict solely productivity. We introduce Sprint2Vec, which leverages three aspects of sprint
information – sprint attributes, issue attributes, and the developers involved in a sprint, to comprehensively characterize it for predicting
both productivity and quality outcomes of the sprints. Our approach combines traditional feature extraction techniques with automated
deep learning-based unsupervised feature learning techniques. We utilize methods like Long Short-Term Memory (LSTM) to enhance
our feature learning process. This enables us to learn features from unstructured data, such as textual descriptions of issues and
sequences of developer activities. We conducted an evaluation of our approach on two regression tasks: predicting the deliverability
(i.e., the amount of work delivered from a sprint) and quality of a sprint (i.e., the amount of delivered work that requires rework). The
evaluation results on five well-known open-source projects (Apache, Atlassian, Jenkins, Spring, and Talendforge) demonstrate our
approach’s superior performance compared to baseline and alternative approaches.