Software reliability modelling using neural network with compounded decreased error rate
Bhoopendra Pachauri, Ajay Kumar, Joydip Dhar · International Journal of Reliability and Safety · 2016
The main task for the software management is to predict the reliability of a software by using an appropriate software reliability growth model (SRGM). The accuracy of the mechanism adopted for reliability prediction is also an important factor. In this study, a software reliability growth modelling problem has been solved using artificial neural networks (ANNs), as ANNs based models are competitive with traditional statistical models for software testing effort estimation. In the first phase, a software reliability growth model has been proposed with compounded decreased error rate to predict the software reliability. The model is further extended by incorporating Cobb-Douglas production function to model the combined effect of time and resources used. Multi-layer feed-forward neural network is used to solve the problems. The weights associated with various input parameters are updated using generalised delta rule and Levenberg-Marquardt method. A comparison is then made with the results from existing models in the literature. The work is validated by using two famous real data sets. Numerical results show that the proposed models perform better in comparison to others existing in the literature.