Software reliability prediction by recurrent artificial chemical link network

Software reliability prediction is the foremost challenge in software quality assurance. Several models have been developed that effectively assess software reliability, but no single model produces accurate prediction results in all situations. This paper proposes a recurrent chemical functional link artificial neural network model to predict the software reliability, where the parameters of the model are estimated by chemical reaction optimization. The proposed model is inheriting the best attributes of functional link artificial neural networks and recurrent neural networks which dynamically modeling a nonlinear system for software reliability prediction. The proposed model is analyzed using ten real-world software failure data. A time-series approach with logarithmic scaling has been adopted for the proper distribution of input data. Statistical analysis reveals that the proposed model exhibits superior performance.