Early software reliability prediction based on support vector machines with genetic algorithms

With recent strong emphasis on rapid development of information technology, the decisions made on the basis of early software reliability estimation can have greatest impact on schedules and cost of software projects. 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. It is also demonstrated that only recent failure data is enough for model training. Two types of model input data selection in the literature are employed to illustrate the performances of various prediction models.