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Effort Prediction in Agile Software Development with Bayesian Networks

The success rate of software projects has been increased since agile methodologies were adopted by many companies. Due their flexibility and continuous communication with clients, the main reason for the failure has shifted from the formulation and understanding of the requirements to inaccurate effort estimation. In recent years, several researchers and practitioners have proposed different estimation techniques. However, some projects are still failing because the budget and/or schedule are not accurately estimated since there still are numerous uncertain variables in software development process. Previous team collaborations, expertise and experience of team members, frequency of changing requirements or priorities are just a few examples. To improve the accuracy of effort estimation, this research proposes a model for agile software development project prediction using Bayesian networks. Based on literature review and practitioners’ knowledge, we identified two major categories of factors that influence effort needed: teamwork quality and user stories characteristics. We identified the sub-factors for each category and inter-dependencies between them. In our model, these factors are the nodes of the directed acyclic graph. The model can help agile teams to obtain a better software effort estimation.