Optimization of Interval Type-2 Fuzzy Logic System for Software Reliability Prediction
DOI:
https://doi.org/10.31695/IJERAT.2020.3665Keywords:
Interval type-2 fuzzy logic, Software reliability, Software metrics, Gradient descent back propagation algorithmAbstract
Since real world application is fraught with high amount of uncertainty, such as applicable to software reliability, there should be a method of handling the uncertainty. This paper presents a model to properly handle uncertainty in software data for effective prediction of the reliability of software at the early phase of software development process. In this paper we employed a Takagi-Sugeno-Kang (TSK)-based interval type 2 fuzzy logic systems with artificial neural network learning for the prediction of software reliability. The degree of membership grades of the interval type 2 fuzzy sets (IT2FSs) are obtained using interval type Gaussian membership function with fixed mean and uncertain standard deviation. The parameters of the IT2FLS membership functions are optimized using gradient descent (GD) back-propagation algorithm. As inputs to the system, reliability relevant software requirement metrics and the software size metrics are used. The proposed new approach makes use of qualitative data of requirement metrics of twenty three real software projects to examine its predictive ability. The performance of the model is evaluated using five performance metrics and found to provide better results when compared with existing approaches.
References
Yadav, H. B. and Yadav,D. K. (2014). Early Software Reliability Analysis using Reliability Relevant Software Metrics. International Journal of System Assurance Engineering and Management, 8(S4) pp 2097-2108
Ravinder, K; Kiran, K. and Arvind K. Measuring Software Reliability : A Fuzzy Model, ACM SIGSOFT Software Engineering Notes, 36(6), (2011), pp. 1-6
IEEE standard glossary of software engineering terminology STD – 729-1991.
Sangeetha.M , Arumugam.C, Sapna P.G and Senthil Kumar .K.M A cooperative approach to ensure software product quality” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1. (2011)
Palviainen, M., Evesti, A. and Ovaska, E., (2011): The reliability estimation prediction and measuring of component-based software. Journal of Systems and Software 84 (6), 1054–1070.
Agrawal M., Chari K. Software Effort, Quality and Cycle Time: A Study of CMM Level 5 Projects, IEEE Transaction on Software Engineering, vol. 33, no. 2, (2007), pp 145-156
Fenton, N., Neil, M., Marsh, W., Hearty, P., Radliński, L., and Krause, P. (2008). On the effectiveness of early life cycle defect prediction with Bayesian Nets. Empirical Software Engineering, 13(5) pp. 499.
Pandey, A. K. and Goyal, N. K. (2009). A Fuzzy Model for Early Software Fault Prediction Using Process Maturity and Software Metrics Reliability Engineering Centre, IIT Kharagpur, INDIA, International Journal of Electronics Engineering, 1(2), pp. 239-245S.
Yadav, D. K., Charurvedi, S. K. and Mishra, R. B. (2012). Early software defects prediction using fuzzy logic, Int. J. Performability Eng. 8 (4) pp399–408S
Rizvi, S. W. A., Khan, R. A. and Singh, V. K. (2016).Software Reliability Prediction using Fuzzy Inference System: Early Stage Perspective, International Journal of Computer Applications (0975 – 8887) Volume 145(10),pp 16-23
Rizvi, S. W. A.; Khan, R. A. and Singh, V. K. (2017). Early Stage Software Reliability Modeling using Requirements and Object-Oriented Design Metrics: Fuzzy Logic Perspective, International Journal of Computer Applications (0975 – 8887) Volume 162, No 2, pp. 44-59
Rizvi, S. W. A. and Khan, R. A. (2009). A Critical Review on Software Maintainability Models. Proceedings of the Conference on Cutting Edge Computer and Electronics Technologies, pp144-148.
Rizvi, S. W. A. and Khan, R. A. (2010). Maintainability Estimation Model for Object-Oriented Software in Design Phase (MEMOOD). Journal of Computing, 2(4), (2010), pp 26-32.
Zhang, X. and Pham, H. (2000). An analysis of factors affecting software reliability. The Journal of Systems and Software 50(1) pp.43–56
Li, M., Smidts, C. and Brill, R. W. (2000). Ranking software engineering measures related to reliability using expert opinion. In: Proceedings of the 11th international symposium on software reliability engineering (ISSRE pp 246–258
Li, M. and Smidts, C. (2003). A ranking of software engineering measures based on expert opinion. IEEE Transactions on Software Engineering, 29(9) (2003) pp811–824
Kumar, T. R., Rao, T. S. and Ch. V. M. K. Hari, A predictive approach to estimate software defects density using Probabilistic Neural Networks for the given Software Metrics, T. Ravi Kumar Journal of Engineering Research and Application ISSN : 2248-9622, Vol. 8, Issue 7 (Part -II) July 2018, (2018) pp 8-15
Cai, K. Y., Wen, C. Y. and Zhang, M. L. (1991). A critical review on software reliability modeling”, Reliability Engineering and System Safety 32 (3) pp. 357–371.
Khatatneh, K. and Mustafa, T. (2009). Software Reliability Modeling Using Soft Computing Technique, in European Journal of Scientific Research ISSN 1450-216X Vol.26 No.1, pp.147-152
Aljahdali, S. (2011). Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic. Journal of Computer Science, 7(10), pp. 1574-1580.
Kumar, R., Khatter, K. and Kalia, A. (2011). Measuring Software Reliability- A Fuzzy Model, ACM SIGSOFT Software Engineering Notes, 36 (6) pp 1-6
Jaikumar, M. and Ramani, A. V. (2017). Software defect prediction using fuzzy logic system. International Journal of Innovations & Advancement in Computer Science, IJIACS, Volume 6, Issue 3, pp. 118-124
Zadeh, L. A. (1965). Fuzzy sets," Information and control, vol. 8, no. 3, pp. 338-353.
Pandey, A. K. and Goyal, N. K. (2010). Fault prediction model by fuzzy profile development of reliability relevant software metrics. International Journal of Computer Applications, 11(6), 34–41.
Yadav, H. B. and Yadav, D. K. (2015). A Fuzzy Logic based Approach for Phase-wise Software Defects Prediction using Software Metrics, Information and Software Technology, 63, pp. 44–57.
Hagras, H. A., (2004): A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots," IEEE Transactions on Fuzzy Systems, vol. 12, no. 4, pp. 524-539.
Hagras, H. (2007) “Type-2 FLCs: A new generation of fuzzy controllers,” IEEE Comput. Intell. Mag., vol. 2, no. 1, pp. 30–43
Mendel, J. M. (2001, July). On the importance of interval sets in type-2 fuzzy logic systems. In Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569) (Vol. 3, pp. 1647-1652). IEEE.
Mendel, J. M. and John, R. B. (2002): Type-2 fuzzy sets made simple," Fuzzy Systems, IEEE Transactions on, vol. 10, no. 2, pp. 117-127
Wu, D., and Mendel, J. M. (2014). Designing practical interval type-2 fuzzy logic systems made simple. In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 800-807.
. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-i," Information Sciences, vol. 8, , pp. 199-249.
Mendel, J. M., John, R. I. and Liu, F. (2006). Interval type-2 fuzzy logic systems made simple," IEEE Transactions on Fuzzy Systems, vol. 14, no. 6, pp. 808-821,.
Chatterjee, S., Maji, B., & Pham, H. (2019). A fuzzy rule-based generation algorithm in interval type-2 fuzzy logic system for fault prediction in the early phase of software development. Journal of Experimental & Theoretical Artificial Intelligence, 31(3), 369-391.
Kayacan, E. and Khanesar, M. (2016). Fuzzy Neural Networks for Real Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning, 720
Hamam, A., &Georganas, N. D. (2008, October). A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications. In 2008 IEEE International Workshop on Haptic Audio visual Environments and Games (pp. 87-92). IEEE.
Eyoh, I. J. (2018). Interval Type-2 Atanassov-Intuitionistic Fuzzy Logic for uncertainty Modelling, Doctoral Dissertation, University of Nottingham p.22
Pandey, A. K. and Goyal, N. K. Multistage model for residual fault prediction, in Early Software Reliability Prediction, Springer, India pp. 59–80.
Lin, Y.-Y., Liao, S.-H., Chang, J.-Y. and Lin, C.-T. (2014). Simplified interval type-2 fuzzy neural networks," IEEE Transactions on Neural Networks and Learning Systems,25(5 ) pp. 959-969.
Begian, M. B., Melek, W. W., & Mendel, J. M. (2008, May). Parametric design of stable type-2 TSK fuzzy systems. In NAFIPS 2008-2008 Annual Meeting of the North American Fuzzy Information Processing Society (pp. 1-6). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Ini Umoeka, Imo Eyoh, Edward Udo, Veronica Akwukwuma
This work is licensed under a Creative Commons Attribution 4.0 International License.
