Measuring reliability of aspect-oriented software using a combination of artificial neural network and imperialist competitive algorithm

Abstract

Aspect-oriented software engineering provides new ways to produce and deliver products and ultimately leads to reliable software. Reliability is an important issue contributing to the quality of software. Thus, software engineers need proven mechanisms to determine the extent of software reliability. In this paper, a method for measuring reliability is proposed which takes advantage of a Multilayer Perceptron Artificial Neural Network (MLPANN). Furthermore, an Imperialist Competitive Algorithm (ICA) is used to optimize the weights to improve network performance. Finally, relying on Root Mean Square Error (RMSE), the proposed approach is compared to a hybrid Genetic Algorithm- Artificial Neural Network (GA-ANN) method. The results show that the proposed approach exhibits lower error.