Maintainability prediction of object-oriented software system by multilayer perceptron model

Abstract

To accomplish software quality, correct estimation of maintainability is essential. However there is a complex and non-linear relationship between object-oriented metrics and maintainability. Thus maintainability of object-oriented software can be predicted by applying sophisticated modeling techniques like artificial neural network. Multilayer Perceptron neural network is chosen for the present study because of its robustness and adaptability. This paper presents the prediction of maintainability by using a Multilayer Perceptron (MLP) model and compares the results of this investigation with other models described earlier. It is found that efficacy of MLP model is much better than both Ward and GRNN network models.