Abstract
In this work, a maintainability prediction model for an object-oriented software system based on type-2 fuzzy logic system is presented. With the proliferation of object-oriented software systems, it has become very essential for concerned organizations to maintain those systems appropriately and effectively. However, it is pathetic to note that just very few number of maintainability prediction models are currently available for object oriented software systems. In this work, maintainability prediction model based on type-2 fuzzy logic systems is developed for an object-oriented software system. Earlier published object-oriented metric dataset was used in building the proposed model. Comparative studies involving the prediction accuracy of the proposed model was carried out in relation to the earlier used models on the same datasets. Empirical results from experiments carried out indicates that the proposed type-2 fuzzy logic system produced better and interesting results in terms of prediction accuracy measures authorized in object oriented software maintainability literatures. In fact, the proposed method satisfies the three major conditions stated in the literatures as basis to determining a good maintainability prediction model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggarwal, K.K., et al.: Application of artificial neural network for predicting maintainability using object-oriented metrics. In: World Academy of Science, Engineering and Technology, vol. 22 (2006)
Zhou, Y., Leung, H.: Predicting object-oriented software maintainability using multivariate adaptive regression splines. J. Syst. Softw. 80(8), 1349–1361 (2007)
Olatunji, S.O., et al.: Extreme learning machine as maintainability prediction model for object-oriented software systems. J. Comput. 2(8), 42–56 (2010)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006). Elsevier
Li, W., Henry, S.: Object-oriented metrics that predict maintainability. J. Syst. Softw. 23(2), 111–122 (1993)
Olatunji, S.O., Selamat, A., Raheem, A.A.A.: A hybrid model through the fusion of type-2 fuzzy logic systems, and extreme learning machines for modelling permeability prediction. Inf. Fusion 2014(16), 29–45 (2014)
Olatunji, S.O., Selamat, A., Raheem, A.A.A.: Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems. Expert Syst. Appl. 38(9), 10911–10922 (2011)
Olatunji, S.O., Selamat, A., Raheem, A.A.: Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems. Comput. Ind. 62(2), 147–163 (2011)
Zarandi, M.H.F., et al.: A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Syst. Appl. 36(1), 139 (2009)
Liang, Q., Mendel, J.M.: Equalization of non-linear time-varying channels using type-2 fuzzy adaptive filters. IEEE Trans. Fuzzy Syst. 8, 551–563 (2000)
Karnik, N.N., Mendel, J.M.: Applications of type-2 fuzzy logic systems to forecasting of time-series. Inf. Sci. 120(1–4), 89–111 (1999)
Olatunji, S.O., Selamat, A., Raheem, A.A.A.: Improved sensitivity based linear learning method for permeability prediction of carbonate reservoir using interval type-2 fuzzy logic system. Appl. Soft Comput. 14, 144–155 (2014)
Liu, F.: An efficient centroid type-reduction strategy for general type-2 fuzzy logic system. Inf. Sci. 178(9), 2224–2236 (2008)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, 1st edn, p. 555. Prentice-Hall, Upper-Saddle River (2001)
Greenfield, S., et al.: The collapsing method of defuzzification for discretised interval type-2 fuzzy sets. Inf. Sci. 179(13), 2055–2069 (2009)
Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1982)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning - I. Inf. Sci. 8, 199–249 (1975)
Liang, Q., Mendel, J.M.: Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8, 535–550 (2000)
Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7, 643–658 (1999)
Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)
Olatunji, S.O.: Data mining in identifying carbonate lithofacies and permeability from well logs based on type-1 and type-2 fuzzy logic inference systems: methodology and comparative studies (MS thesis). In: Information and Computer Science. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, p. 179 (2008)
Yu-ching, L., Ching-hung, L.: System identification and adaptive filter using a novel fuzzy neuro system. Int. J. Comput. Cogn. 5(1), 1–12 (2007)
Chen, X., et al.: Type-2 fuzzy logic-based classifier fusion for support vector machines. Appl. Soft Comput. 8(3), 1222–1231 (2008)
Mendel, J.M.: Fuzzy sets for words: a new beginning. In: The 12th IEEE International Conference on Fuzzy Systems, Los Angeles (2003)
Mendel, J.M.: Type-2 fuzzy sets: some questions and answers. IEEE Connections Newsl. IEEE Neural Netw. Soc. 1, 10–13 (2003)
Gray, A.R., MacDonell, S.G.: A comparison of techniques for developing predictive models of software metrics. Inf. Softw. Technol. 39, 425–437 (1997)
van Koten, C., Gray, A.R.: An application of Bayesian network for predicting object-oriented software maintainability. Inf. Softw. Technol. 48(1), 59–67 (2006)
Fenton, N.E., Pfleeger, S.L.: Software Metrics: A Rigorous and Practical Approach. PWS Publishing Co., Boston (1998)
Conte, S.D., Dunsmore, A.H.E., Shen, A.V.Y.: Software Engineering Metrics and Models. Benjamin-Cummings Publishing Co. Inc., Boston (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Olatunji, S.O., Selamat, A. (2015). Type-2 Fuzzy Logic Based Prediction Model of Object Oriented Software Maintainability. In: Fujita, H., Selamat, A. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2014. Communications in Computer and Information Science, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-17530-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-17530-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17529-4
Online ISBN: 978-3-319-17530-0
eBook Packages: Computer ScienceComputer Science (R0)