Software project dynamics: an integrated approach
Software project dynamics: an integrated approach
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Machine Learning
Computer Science Today: Recent Trends and Developments
Computer Science Today: Recent Trends and Developments
An Empirical Study of Analogy-based Software Effort Estimation
Empirical Software Engineering
Capability Maturity Model, Version 1.1
IEEE Software
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
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Decision making has been traditionally based on a managers experience. This paper, however, discusses how a software project simulator based on System Dynamics and Evolutionary Computation can be combined to obtain management rules. The purpose is to provide accurate decision rules to help project managers to make decisions at any time in the software development life cycle. To do so, a database from which management rules are generated is obtained using a software project simulator based on system dynamics. We then find approximate optimal management rules using an evolutionary algorithm which implements a novel method for encoding the individuals, i.e., management rules to be searched by the algorithm. The resulting management rules of our method are also compared with the ones obtained by another algorithm called C4.5. Results show that our evolutionary approach produces better management decision rules regarding quality and understandability.