Towards a generic model for software quality prediction
Proceedings of the 6th international workshop on Software quality
Software metrics data clustering for quality prediction
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
On the dataset shift problem in software engineering prediction models
Empirical Software Engineering
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The use of the statistical technique of mixture model analysis as a tool for early prediction of fault-prone program modules is investigated. The Expectation-Maximum likelihood (EM) algorithm is engaged to build the model. By only employing software size and complexity metrics, this technique can be used to develop a model for predicting software quality even without the prior knowledge of the number of faults in the modules. In addition, Akaike Information Criterion (AIC) is used to select the model number, which is assumed the class numbers the program modules should be classified. The technique is successful in classifying software into fault-prone and non fault-prone modules with a relatively low error rate, providing a reliable indicator for software quality prediction.