Misclassification cost-sensitive fault prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
A survey in the area of machine learning and its application for software quality prediction
ACM SIGSOFT Software Engineering Notes
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Current software quality estimation models often involve the use of supervised learning methods for building a software fault prediction models. In such models, dependent variable usually represents a software quality measurement indicating the quality of a module by risk-basked class membership, or the number of faults. Independent variables include various software metrics as McCabe, Error Count, Halstead, Line of Code, etc... In this paper we present the use of advanced tool for data mining called Multimethod on the case of building software fault prediction model. Multimethod combines different aspects of supervised learning methods in dynamical environment and therefore can improve accuracy of generated prediction model. We demonstrate the use Multimethod tool on the real data from the Metrics Data Project Data (MDP) Repository. Our preliminary empirical results show promising potentials of this approach in predicting software quality in a software measurement and quality dataset.