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An ensemble-based model for predicting agile software development effort
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DOI：10.1007/s10664-018-9647-0.png)
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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.
Keyword:
Agile
Effort prediction
Ensemble
Machine learning
Scrum
Sprint planning
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