Document Type

Conference Proceeding

Publisher

Springer

Faculty

Faculty of Health, Engineering and Science

School

School of Computer and Security Science

RAS ID

17829

Comments

This article was originally published as: Baqais, A., Alshayeb, M., & Baig, Z. A. (2014). Hybrid intelligent model for software maintenance prediction . Proceedings of World Congress on Engineering. (pp. 358-362). London, U.K. Springer. Original article available here

Abstract

Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used for evolving the neural network topology until an optimal topology is reached. The model was applied on a popular open source program, namely, Android. The results are very promising, where the correlation between actual and predicted points reaches 0.91

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