Abstract
Software reliability prediction is the foremost challenge in software quality assurance. Several models have been developed that effectively assess software reliability, but no single model produces accurate prediction results in all situations. This paper proposes a recurrent chemical functional link artificial neural network model to predict the software reliability, where the parameters of the model are estimated by chemical reaction optimization. The proposed model is inheriting the best attributes of functional link artificial neural networks and recurrent neural networks which dynamically modeling a nonlinear system for software reliability prediction. The proposed model is analyzed using ten real-world software failure data. A time-series approach with logarithmic scaling has been adopted for the proper distribution of input data. Statistical analysis reveals that the proposed model exhibits superior performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Benala TR, Chinnababu K, Mall R, Dehuri S (2013) A particle swarm optimized functional link artificial neural network (PSO-FLANN) in software cost estimation. In: Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA), Springer, Berlin, Heidelberg, pp 59–66 https://doi.org/10.1007/978-3-642-35314-7_8
Behera AK, Panda M (2019) Software reliability prediction with ensemble method and virtual data point incorporation. International conference on biologically inspired techniques in many-criteria decision making. Springer, Cham, pp 69–77. https://doi.org/10.1007/978-3-030-39033-4_7
Behera AK, Nayak SC, Dash CSK, Dehuri S, Panda M (2019) Improving software reliability prediction accuracy using CRO-based FLANN. Innovations in computer science and engineering. Springer, Singapore, pp 213–220. https://doi.org/10.1007/978-981-10-8201-6_24
Bhuyan MK, Mohapatra DP, Sethi S (2016) Software reliability assessment using neural networks of computational intelligence based on software failure data. Baltic J Modern Comput 4(4):1016–1037. https://doi.org/10.22364/bjmc.2016.4.4.26
Bisi M, Goyal NK (2015) Prediction of software inter-failure times using artificial neural network and particle swarm optimisation models. Int J Soft Eng Technol Appl 1(2–4):222–244. https://doi.org/10.1504/IJSETA.2015.075629
Bisi M, Goyal NK (2016) Software development efforts prediction using artificial neural network. IET Software 10(3):63–71. https://doi.org/10.1049/iet-sen.2015.0061
Cai KY, Cai L, Wang WD, Yu ZY, Zhang D (2001) On the neural network approach in software reliability modeling. J Syst Softw 58(1):47–62. https://doi.org/10.1016/S0164-1212(01)00027-9
Corder GW, Foreman DI (2014) Nonparametric statistics: A step-by-step approach. John Wiley & Sons, Inc., Hoboken, New Jersey, ISBN 978-1-118-84031-3
Huang CY, Lyu MR (2011) Estimation and analysis of some generalized multiple change-point software reliability models. IEEE Trans Reliab 60(2):498–514
Dehuri S, Cho SB (2010) Evolutionarily optimized features in functional link neural network for classification. Expert Sys Appl 37(6):4379–4391. https://doi.org/10.1016/j.eswa.2009.11.090
El-Sebakhy EA (2009) Software reliability identification using functional networks: a comparative study. Expert Sys appl 36(2):4013–4020. https://doi.org/10.1016/j.eswa.2008.02.053
Iyer RK, Lee I (1996) Measurement-based analysis of software reliability. Handbook of software reliability engineering. IEEE Computer Society Press, Los Alamitos, California, pp 303–358
Jaiswal A, Malhotra R (2018) Software reliability prediction using machine learning techniques. Int J Sys Assur Eng Manag 9(1):230–244. https://doi.org/10.1007/s13198-016-0543-y
Juneja K (2019) A fuzzy-filtered neuro-fuzzy framework for software fault prediction for inter-version and inter-project evaluation. Appl Soft Comput 77:696–713. https://doi.org/10.1016/j.asoc.2019.02.008
Karunanithi N, Whitley D, Malaiya YK (1992) Using neural networks in reliability prediction. IEEE Softw 9(4):53–59. https://doi.org/10.1109/52.143107
Kiran NR, Ravi V (2008) Software reliability prediction by soft computing techniques. J Syst Softw 81(4):576–583. https://doi.org/10.1016/j.jss.2007.05.005
Lakshmanan I, Ramasamy S (2015) An artificial neural-network approach to software reliability growth modeling. Procedia Comput Sci 57:695–702
Lam AY, Li VO (2009) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399. https://doi.org/10.1109/TEVC.2009.2033580
Littlewood B, Salako K, Strigini L, Zhao X (2020) On reliability assessment when a software-based system is replaced by a thought-to-be-better one. Reliab Eng Syst Saf 197:106752. https://doi.org/10.1016/j.ress.2019.106752
Mallikharjuna RK, Kodali A (2017) An efficient method for enhancing reliability and selection of software reliability growth model through optimization techniques. JSW 12(1):1–8. https://doi.org/10.17706/jsw.12.1.1-18
Mohanty R, Ravi V, Patra MR (2013) Hybrid intelligent systems for predicting software reliability. Appl Soft Comput 13(1):189–200. https://doi.org/10.1016/j.asoc.2012.08.015
Nayak SC, Misra BB, Behera HS (2017) Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices. Ain Shams Eng J 8(3):371–390. https://doi.org/10.1016/j.asej.2015.07.015
Nayak SC, Misra BB, Behera HS (2019) ACFLN: artificial chemical functional link network for prediction of stock market index. Evol Syst 10(4):567–592. https://doi.org/10.1007/s12530-018-9221-4
Nayak SC (2021) Bitcoin closing price movement prediction with optimal functional link neural networks. Evol Intel. https://doi.org/10.1007/s12065-021-00592-z
Pai PF, Hong WC (2006) Software reliability forecasting by support vector machines with simulated annealing algorithms. J Sys Softw 79(6):747–755. https://doi.org/10.1016/j.jss.2005.02.025
Pandey AK, Goyal NK (2015) Early software reliability prediction. Springer, India
Pandey SK, Mishra RB, Tripathi AK (2020) BPDET: an effective software bug prediction model using deep representation and ensemble learning techniques. Expert Syst Appl 144:113085. https://doi.org/10.1016/j.eswa.2019.113085
Pao YH, Takefuji Y (1992) Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5):76–79. https://doi.org/10.1109/2.144401
Park J, Baik J (2015) Improving software reliability prediction through multi-criteria based dynamic model selection and combination. J Syst Softw 101:236–244. https://doi.org/10.1016/j.jss.2014.12.029
Qin LN (2011) Software reliability prediction model based on PSO and SVM. In: 2011 International conference on consumer electronics, communications and networks (CECNet). IEEE. https://doi.org/10.1109/CECNET.2011.5768285, pp 5236–5239
Rao BT, Dehuri S, Mall R (2012) Functional link artificial neural networks for software cost estimation. Int J Appl Evol Comput (IJAEC) 3(2):62–82. https://doi.org/10.4018/jaec.2012040104
Roy P, Mahapatra GS, Dey KN (2015) Neuro-genetic approach on logistic model- based software reliability prediction. Expert Sys Appl 42(10):4709–4718. https://doi.org/10.1016/j.eswa.2015.01.043
Roy P, Mahapatra GS, Dey KN (2019) Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network. IEEE/CAA J Automatica Sinica 6(6):1365–1383. https://doi.org/10.1109/JAS.2019.1911753
Shanmugam L, Florence L (2013) Enhancement and comparison of ant colony optimization for software reliability models. Citeseer. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.678.9499.
Shen Q, Lou J, Zhang X, Jiang Y (2021) Failure prediction by regularized fuzzy learning with intelligent parameters selection. Appl Soft Comput 100:106952. https://doi.org/10.1016/j.asoc.2020.106952
Shi Y, Li M, Arndt S, Smidts C (2017) Metric-based software reliability prediction approach and its application. Empir Softw Eng 22(4):1579–1633. https://doi.org/10.1007/s10664-016-9425-9
Sudharson D (2020) Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO). Int J Intell Unmanned Syst. https://doi.org/10.1108/IJIUS-09-2019-0052
Tian L, Noore A (2005) Evolutionary neural network modeling for software cumulative failure time prediction. Reliab Eng Syst Saf 87(1):45–51. https://doi.org/10.1016/j.ress.2004.03.028
van Driel WD, Bikker JW, Tijink M (2021) Prediction of software reliability. Microelectron Reliab 119:114074. https://doi.org/10.1016/j.microrel.2021.114074
Zemouri R, Zerhouni N (2012) Autonomous and adaptive procedure for cumulative failure prediction. Neural Comput Appl 21(2):319–331. https://doi.org/10.1007/s00521-011-0585-7
Acknowledgements
The authors would like to thank all the anonymous reviewers for their valuable comments and suggestions.
Funding
The authors declare that they have received no funding from any source for doing the research and/or publication of this article.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Behera, A.K., Panda, M. & Dehuri, S. Software reliability prediction by recurrent artificial chemical link network. Int J Syst Assur Eng Manag 12, 1308–1321 (2021). https://doi.org/10.1007/s13198-021-01276-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-021-01276-8