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Multilayer Neural Network and Fuzzy Logic Based Software Quality Prediction

Authors: Sadaf Sahar, Usman Qamar, Sadaf Ayaz

Abstract:

In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training.

Keywords: Software quality, fuzzy logic, perceptron, prediction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132086

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References:


[1] T. M. Khoshgoftaar and N. Seliya “Analogy-based practical classification rules for software quality estimation”, Empirical Software Engineering, 8(4): 325-350, 2003.
[2] T. M. Khoshgoftaar, Y. Liu and N. Seliya “A multiobjective module-order model for software quality enhancement”, IEEE Transactions on Evolutionary Computation, 8(6): 593-608, 2004.
[3] T. M. Khoshgoftaar, E. B. Allen, W. D. Jones and J. P. Hudepohl “Classification-tree models of software-quality over multiple releases”, IEEE Transactions on Reliability.
[4] T. M. Khoshgoftaar, R. M. Szabo and P. J. Guasti “Exploringthe behaviour of neural network software quality models”, Software Engineering Journal, 10(3): 89-96, 1995.
[5] M. M. T. Thwin and T. S. Quah “Application of neural networks for software quality prediction using object-oriented metrics”, Journal of Systems and Software, 76(2): 147-156, 2005.
[6] S. S. So, S. D. Cha and Y. R. Kwon “Empirical evaluation of a fuzzy logic-based software quality prediction model”, Fuzzy Sets and Systems, 127(2): 199-208, 2002.
[7] N. E. Fenton and M. Neil “A critique of software defect prediction models”, IEEE Transactions on Software Engineering, 25(5): 675-689, 1999.
[8] E. Khan, Neural Fuzzy Based Intelligent Systems and Applications, in Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications, by L.C. Jain and N.M. Martin, Chapter 5, CRC Press, 1998.
[9] M. B. Ghalia and A. T. Alouani “Artificial neural networks and fuzzy logic for system modeling and control: a comparative study”, in Proceedings of the 27th Southeastern Symposium on System Theory, pp. 258-262, March 1995.
[10] C. T. Lin and C. S. G. Lee “Neural-network-based fuzzy logic control and decision system”, IEEE Transactions on Computers, 40(12): 1320-1336, 1991.
[11] C. J. Lin and C.T. Lin “An ART-based fuzzy adaptive learning control network” IEEE Transactions on Fuzzy Systems, 5(4): 477-496, 1997.
[12] J. C. Munson and T. M. Khoshgoftaar, “The dimesionality of program complexity,” in Proceedings of the 11th International Conference on Software Engineering, pp. 245-253, Pittsburgh, PA, May 1989.
[13] S. R. Chidamber and C. F. Kemerer, “A Metrics Suite for Object-Oriented Design,” IEEE Transactions on Software Engineering, vol. 20, no. 6, pp. 476–493, 1994.
[14] B. Henderson-Sellers, Object-oriented metrics: measures of complexity.Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1996.
[15] W. Li and S. Henry, “Maintenance metrics for the object oriented paradigm,” in IEEE Proceedings of the First International Software Metrics Symposium, May 1993, pp. 52–60.
[16] R. Bandi, V. Vaishnavi, and D. Turk, “Predicting maintenance per-formance using object-oriented design complexity metrics,” Software Engineering, IEEE Transactions on, vol. 29, no. 1, pp. 77–87, Jan. 2003.
[17] V. R. Basili, L. C. Briand, and W. L. Melo, “A Validation of Object-Oriented Design Metrics as Quality Indicators,” IEEE Trans. Softw. Eng., vol. 22, no. 10, pp. 751–761, 1996.
[18] P. Yu, T. Systa, and H. Muller, “Predicting fault-proneness using oo met-rics. an industrial case study,” Software Maintenance and Reengineering, 2002. Proceedings. Sixth European Conference on, pp. 99–107, 2002.
[19] T. Gyim ´ othy, R. Ferenc, and I. Siket, “Empirical validation of object-oriented metrics on open source software for fault prediction,” IEEE Trans. on Software Engineering, vol. 31, no. 10, pp. 897–910, 2005.
[20] R. Subramanyam and M. S. Krishnan, “Empirical Analysis of CK Met-rics for Object-Oriented Design Complexity: Implications for Software Defects,” IEEE Trans. Softw. Eng., vol. 29, no. 4, pp. 297–310, 2003.