An Efficient Technique for Optimization of Test Cases with the Aid of HNN-MCS for Assessing Software Reliability

Abstract: The advance in software development has resulted in need for efficient and reliable software products. In recent the growth of software demands high reliability and safety, software reliability prediction becomes more and more essential. Software reliability is a key part of software quality. Various techniques for predicting software reliability have been proposed and evaluated in terms of their prediction performance; however, their actual contribution to business objectives such as quality improvement and cost reduction has been rarely assessed. The main aim of this work is to develop an efficient software reliability prediction method where soft computing is utilized. We are proposing a novel method of reliability prediction with the aid of Hybrid Neural network incorporated with optimization algorithm (HNN-MCS). The weight factor is globally optimized using the modified cuckoo search algorithm. Once the training is done the data are then tested in order to check the prediction accuracy of the proposed systems. Researchers considered different factors as inputs for training the network. The execution time is utilized in our proposed system for training the neural network and based on this the testing is done. The results in terms of actual and predicted failure rate are estimated. Keywords: Technique for Optimization of Test Cases with the Aid of HNN-MCS