Affiliations: [a] Information Systems, School of Business Administration, Capital College, Pennsylvania State University, 777 W. Harrisburg Pike, Middletown, PA, USA | [b] MIS and Decision Sciences, Eberly College of Business, Indiana University of Pennsylvania, Indiana, PA, USA
Abstract: Labor cost is one of the major contributors of software development cost. Among the variables that affects labor cost is the software development team size. There are very few methods available in literature to determine software development team size because team size selection happens during early stages of software development, and varies throughout systems development cycle. Under such circumstances, only methods that can be used to predict team size are Bayesian and analytical methods. In this paper, we use both Bayesian and analytical methods to predict team size. Specifically, we use a hybrid Bayesian network and simulation methodology for estimating posterior distributions of the team size using a real-world software engineering dataset, and a Cobb-Douglas function to estimate optimal team size. Using the leave-one-out sampling, we test our Bayesian approach and find that our approach predicts appropriate team size category with over 90% accuracy. However, our tests with optimal team size indicate that less than 20% of real-world software projects use optimal team size.
Keywords: Analytical modeling, decision support systems, software development methodologies, Bayesian networks, artificial intelligence, probabilistic reasoning
