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Investigation of Software Reliability Prediction Using Statistical and Machine Learning Methods
Abstract
Software reliability is a statistical measure of how well software operates with respect to its requirements. There are two related software engineering research issues about reliability requirements. The first issue is achieving the necessary reliability, i.e., choosing and employing appropriate software engineering techniques in system design and implementation. The second issue is the assessment of reliability as a method of assurance that precedes system deployment. In past few years, various software reliability models have been introduced. These models have been developed in response to the need of software engineers, system engineers and managers to quantify the concept of software reliability. This chapter investigates performance of some classical and intelligent machine learning techniques such as Linear regression (LR), Radial basis function network (RBFN), Generalized regression neural network (GRNN), Support vector machine (SVM), to predict software reliability. The effectiveness of LR and machine learning methods is demonstrated with the help of sixteen datasets taken from Data & Analysis Centre for Software (DACS). Two performance measures, root mean squared error (RMSE) and mean absolute percentage error (MAPE) is compared quantitatively obtained from rigorous experiments.
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