Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for measuring information system size
MIS Quarterly
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Artificial intelligence: theory and practice
Artificial intelligence: theory and practice
An information theoretic technique to design belief network based expert systems
Decision Support Systems - Special issue: workshop on information technology and systems (WITS '93)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Conference on The Future of Software Engineering
Proceedings of the Conference on The Future of Software Engineering
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
ACM SIGKDD Explorations Newsletter
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Constructing Efficient Belief Network Structures With Expert Provided Information
IEEE Transactions on Knowledge and Data Engineering
Estimating Software Development Effort with Case-Based Reasoning
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
AI Magazine
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
An empirical evaluation of a testing and debugging methodology for Excel
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Learning Bayesian Networks
A dynamic topological sort algorithm for directed acyclic graphs
Journal of Experimental Algorithmics (JEA)
Software project management with GAs
Information Sciences: an International Journal
An empirical study of the impact of team size on software development effort
Information Technology and Management
An approach to optimizing software development team size
Information Processing Letters
An empirical study of the Cobb-Douglas production function properties of software development effort
Information and Software Technology
Journal of Cases on Information Technology
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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.