An ensemble-based model for predicting agile software development effort

To support agile software development projects, an array of tools and systems is available to plan, design, track, and manage the development process. In this paper, we explore a critical aspect of agile development i.e., effort prediction, that cuts across these tools and agile project teams. Accurate effort prediction can improve the planning of a sprint by enabling optimal assignments of both stories and developers. We develop a model for story-effort prediction using variables that are readily available when a story is created. We use seven predictive algorithms to predict a story’s effort. Interestingly, none of the predictive algorithms consistently outperforms others in predicting story effort across our test data of 423 stories. We develop an ensemble-based method based on our model for predicting story effort. We conduct computational experiments to show that our ensemble-based approach performs better in comparison to other ensemble-based benchmarking approaches. We then demonstrate the practical application of our predictive model and our ensemble-based approach by optimizing sprint planning for two projects from our dataset using an optimization model.