Software engineering (SE) is concerned with designing, developing and maintaining programs that behave reliably and efficiently. SE is composed of various phases which software goes through during and after its development. In today’s prospects, predicting quality of software is a quite challenging task. This study focuses on a recent bio-inspired algorithm named shuffled frog-leaping algorithm (SFLA) for estimating parameters of software reliability growth models (SRGM). Most of the bio-inspired algorithms are inspired by some real-world phenomenon, generally a natural method of optimization. Over the last few decades, a number of bio-inspired algorithms have been introduced and applied on various problems of different domains. SFLA embeds features of particle swarm optimization (PSO) and shuffled complex evolution (SCE) algorithms. It is evident from the literature that SFLA can be efficiently applied to solve various engineering design problems. A variant of SFLA named O-SFLA that embeds opposition-based learning is also introduced in this study. This variant has been evaluated on a set of benchmark problems, which have been verified by performing nonparametric analysis. Later the application of O-SFLA has been carried out on estimating parameters of software reliability growth models (SRGM).
- Shuffled frog-leaping algorithm
- Opposition-based learning
- Software reliability growth models
- Global optimization
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
J. Musa, A. Iannino, and K. Okumoto. Software Reliability: Measurement, Prediction, Applications. McGraw Hill, 1987.
H. Pham. Software Reliability. Springer-Verlag, 2000.
P. G. Bishop and R. Bloomfield. Worst case reliability prediction on a prior estimate of residual defects. In Proceedings of the 13th IEEE International Symposium on Software Reliability Engineering (ISSRE-2002), pages 295–303, 2002.
M.M. Eusuff, K.E. Lansey, Optimization of water distribution network design using the shuffled frog leaping algorithm, J. Water Resour. Plan. Manage 129 (2003) 210–225.
M. Eusuff, K. Lansey, F. Pasha, Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization, Eng. Optim. 38 (2006) 129–154.
M. Xie. Software reliability models - past, present and future. In N. Limnios and M. Nikulin (Eds). Recent Advances in Reliability Theory: Methodology, Practice and Inference, pages 323–340, 2002.
S. Yamada. Software reliability models and their applications: A survey. In International Seminar on Software Reliability of Man Machine Systems—Theories Methods and Information Systems Applications - August 17–18, Kyoto University, Kyoto, Japan, 2000.
P. B. Moranda. Predictions of software reliability during debugging. In Proceedings of Annual Reliability and Maintainability Symposium, pages 327–332, 1975.
J. Musa. A theory of software reliability and its application. IEEE Trans. Software Engineering, 1:312–327, 1975.
L. H. Crow. Reliability for complex repairable systems. Reliability and Biometry, SIAM, pages 379–410, 1974.
S. Yamada, M. Ohba, and Osaki S. S-Shaped software reliability growth models and their applications. IEEE Trans. Reliability, pages 289–292, 1984.
M.A. Ahandani, H. Alavi-Rad, Opposition-based learning in the shuffled differential evolution algorithm, Soft Comput. 16 (2012) 1303–1337.
J. Li, Q. Pan, S. Xie, An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems, Appl. Math. Comput. 218 (2012) 9353–9371.
F. Tang-Huai, L. Li, Z. Jia, Improved shuffled frog leaping algorithm and its application in node localization of wireless sensor network, Intell. Autom. Soft Comput. 18 (2012) 807–818.
M.A. Ahandani, H. Alavi-Rad, Opposition-based learning in shuffled frog leaping: An application for parameter identification, Information Sciences 291 (2015) 19–42.
Tarun Kumar Sharma, Millie Pant, Shuffled Artificial Bee Colony Algorithm. Soft Computing, 21 (2017) 6085–6104.
Tarun Kumar Sharma, Millie Pant, Identification of noise in multi noise plant using enhanced version of shuffled frog leaping algorithm, International Journal of Systems Assurance Engineering and Management, Springer (https://doi.org/10.1007/s13198-016-0466-7), 2016.
Tarun Kumar Sharma, Millie Pant, Opposition based learning ingrained shuffled frog-leaping algorithm, Journal of Computational Science 21 (2017) 307–315.
Tarun Kumar Sharma and Millie Pant, Opposition Based Learning Embedded Shuffled Frog-Leaping Algorithm. In Proceedings of International Conference on Soft Computing: Theories and Applications Volume 2 of the series Advances in Intelligent Systems and Computing, 2016.
Chao Liu, Peifeng Niu, Guoqiang Li, Yunpeng Ma, Weiping Zhang, Ke Chen. Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems. Journal of Intelligent Manufacturing, 1–21, 2015.
Pasura Aungkulanon, Pongchanun Luangpaiboon. Vertical transportation systems embedded on shuffled frog leaping algorithm for manufacturing optimisation problems in industries. Springer Plus, https://doi.org/10.1186/s40064-016-2449-1, 2016.
Haorui Liu, Fengyan Yi, Heli Yang. Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems. Computational Intelligence and Neuroscience, Volume 2016 (2016), Article ID 5675349.
Amol M. Dalavi, Padmakar J. Pawar, Tejinder Paul Singh. Tool path planning of hole-making operations in ejector plate of injection mould using modified shuffled frog leaping algorithm. Journal of Computational Design and Engineering, Volume 3, Issue 3, July 2016, Pages 266–273.
Deming Lei, Xiuping Guo. A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents. Expert Systems with Applications, Volume 42, Issue 23, 15 December 2015, Pages 9333–9339.
Morteza Jadidoleslam, Akbar Ebrahimi. Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm. International Journal of Electrical Power & Energy Systems, Volume 64, January 2015, Pages 743–751.
H.R. Tizhoosh, Opposition-based learning: a new scheme for machine intelligence, in: Proc. Int. Conf. Comput. Intell. Modeling, Control and Autom., Vienna, Austria, 2005, pp. 695–701.
Jia Zhao; Li Lv. Shuffled frog-leaping algorithm using elite opposition-based learning. Int. J. of Sensor Networks, 2014 Vol. 16, No. 4, pp. 244–251.
Demšar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res, (2006), 7:1–30.
García S., Herrera F. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons, J Mach Learn Res, (2008), 9:2677–2694.
Dunn OJ. Multiple comparisons among means, J Am Stat Assoc,(1961),56(293):52–64.
A. Sheta. Estimation of the COCOMO model parameters using genetic algorithms for NASA software projects. Journal of Computer Science, USA, 2(2):118–123, 2006.
Editors and Affiliations
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sharma, T.K. (2018). Estimating Software Reliability Growth Model Parameters Using Opposition-Based Shuffled Frog-Leaping Algorithm. In: Ray, K., Pant, M., Bandyopadhyay, A. (eds) Soft Computing Applications. Studies in Computational Intelligence, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-8049-4_8
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8048-7
Online ISBN: 978-981-10-8049-4