Handbook of software reliability engineering
Handbook of software reliability engineering
Software metrics: success, failures and new directions
Journal of Systems and Software - Special issue on invited articles on top systems and software engineering scholars
Software Quality Prediction Using Mixture Models with EM Algorithm
APAQS '00 Proceedings of the The First Asia-Pacific Conference on Quality Software (APAQS'00)
An Empirical Study on Testing and Fault Tolerance for Software Reliability Engineering
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
An Empirical Study on Reliability Modeling for Diverse Software Systems
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Cluster number selection for a small set of samples using the Bayesian Ying-Yang model
IEEE Transactions on Neural Networks
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Software metrics are collected at various phases of the software development process. These metrics contain the information of software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which is one of data mining techniques, is adopted to build the software quality prediction models in early period of software testing. In this paper, three clustering methods, k-means, fuzzy c-means and Gaussian mixture model, are investigated for the analysis of two real-world software metric datasets. The experiment results show that the best method in predicting software quality is dependent on practical dataset, and clustering analysis technique has advantages in software quality prediction since it can be used in the case having little prior knowledge.