Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models

Software reliability is the probability of failure-free software operation for a specified period of time in a specified environment. During the last three decades, many software reliability growth models (SRGMs) have been proposed and analyzed for measuring software reliability growth. SRGMs are mathematical models that represent software failures as a random process and can be used to evaluate development status during testing. However, most of SRGMs depend on some assumptions or distributions. In this paper, we propose an artificial neural-network-based approach for software reliability estimation and modeling. We first explain the neural networks from the mathematical viewpoints of software reliability modeling. We will show how to apply neural network to predict software reliability by designing different elements of neural networks. Furthermore, we will use the neural network approach to build a dynamic weighted combinational model (DWCM). The applicability of proposed model is demonstrated through real software failure data sets. The results obtained from the experiments show that the proposed model has a fairly accurate prediction capability.