A study of applying support vector machine and Genetic Algorithm to software reliability forecasting

Software reliability prediction models is very helpful for developers and testers to know the phase in which corrective action need to be performed in order to achieve target reliability estimate. In this paper, an SVM-based model for software reliability forecasting is proposed. Support vector machine (SVM) is a new method based on statistical learning theory. It has been successfully used to solve nonlinear regression and time series problems. However, SVM has rarely been applied to software reliability prediction. In addition, the parameters of SVM are determined by Genetic Algorithm (GA). It is also demonstrated that only recent failure data is enough for model training. This feature that the model does not use all available failure data enables software developers and testers to obtain general ideas about software reliability in the early phase of testing process. Two types of model input data selection in the literature are employed to illustrate the performances of various prediction models. Empirical results show that the proposed model is more precise in its reliability prediction and is less dependent on the size of failure data comparing with the other forecasting models.