Abstract
Accurate estimation of attributes such as effort, quality and risk is of major concern in software life cycle. Majority of the approaches available in literature for estimation are based on regression analysis and neural network techniques. In this study, Chidamber and Kemerer software metrics suite has been considered to provide requisite input data to train the artificial intelligence models. Two artificial intelligence (AI) techniques have been used for predicting maintainability viz., neural network and neuro-genetic algorithm (a hybrid approach of neural network and genetic algorithm). These techniques are applied for predicting maintainability on a case study i.e., Quality Evaluation System (QUES) and User Interface System (UIMS). The performance was evaluated based on the different performance parameters available in literature such as: Mean Absolute Relative Error (MARE), Mean absolute error (MAE), Root Mean Square Error (RMSE), and Standard Error of the Mean (SEM) etc. It is observed that the hybrid approach utilizing Neuro-GA achieved better result for predicting maintainability when compared with that of neural network.
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Abreu, F.B.E., Carapuca, R.: Object-oriented software engineering: Measuring and controlling the development process. In: Proceedings of the 4th International Conference on Software Quality, vol. 186 (1994)
Battiti, R.: First and second-order methods for learning between steepest descent and newton’s method. Neural Computation 4(2), 141–166 (1992)
Burgess, C., Lefley, M.: Can genetic programming improve software effort estimation. Information and Software Technology 43, 863–873 (2001)
Chidamber, S.R., Kemerer, C.F.: A metrics suite for object-oriented design. IEEE Transactions on Software Engineering 20(6), 476–493 (1994)
Coleman, D., Ash, D., Lowther, B., Oman, P.: Using metrics to evaluate software system maintainability. IEEE Computer 27(8), 44–49 (1994)
Halstead, M.: Elements of Software Science. Elsevier Science, New York (1977)
Jung, H.W., Kim, S.G., Chung, C.S.: Measuring software product quality: A survey of iso/iec 9126. IEEE Software 21(5), 88–92 (2004)
Briand, L.C., Wust, J., Daly, J.W., Porter, D.V.: Exploring the relationships between design measures and software quality in object-oriented systems. The Journal of Systems and Software 51(3), 245–273 (2000)
Li, W., Henry, S.: Maintenance metrics for the object-oriented paradigm. In: Proceedings of First International Software Metrics Symposium, pp. 88–92 (1993)
Lorenz, M., Kidd, J.: Object-Oriented Software Metrics. Prentice-Hall, Englewood (1994)
McCabe, T.J.: A complexity measure. IEEE Transactions on Software Engineering 2(4), 308–320 (1976)
McCulloch, W., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5(4), 115–133 (1943)
Oman, P., Hagemeister, J.: Construction and testing of polynomials predicting software maintainability. Journal of Systems and Software 24(3), 251–266 (1994)
Schneberger, S.L.: Distributed computing environments: effects on software maintenance difficulty. Journal of Systems and Software 37(2), 101–116 (1997)
Van Koten, C., Gray, A.: An application of bayesian network for predicting object-oriented software maintainability. Information and Software Technology 48(1), 59–67 (2006)
Zhou, Y., Leung, H.: Predicting object-oriented software maintainability using multivariate adaptive regression splines. Journal of Systems and Software 80(8), 1349–1361 (2007)
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Kumar, L., Rath, S.K. (2015). Neuro – Genetic Approach for Predicting Maintainability Using Chidamber and Kemerer Software Metrics Suite. In: Unger, H., Meesad, P., Boonkrong, S. (eds) Recent Advances in Information and Communication Technology 2015. Advances in Intelligent Systems and Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-19024-2_4
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DOI: https://doi.org/10.1007/978-3-319-19024-2_4
Publisher Name: Springer, Cham
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