Abstract
Software reliability is the important attribute for complex computing systems to provide reliability could cause series issues such as extra cost, development delay and image of the software solution providers. Hence, ensuring the reliability of software before deliver to the customer is essential part for the company. Finding the error in right time with reasonable degree of accuracy helps to prevent the consequences. Several software reliability growth models developed and used to measure the trustworthiness based on development and testing phases with unrealistic assumption over the environment and applied Block box methodologies while constructing model. This paper presents well established statistical time series (S)ARIMA approach for developing a forecasting model that able to provide significantly improved reliability prediction. Using real time publicly available software failure sets, the prediction of proposed model is developed and compared with previously available reliability models.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alweshah M, Ahmed W, Aldabbas H (2015) Evolution of software reliability growth models: a comparison of auto-regression and genetic programming models. Int J Comput Appl 125(3):20–25
Amin A, Grunske L, Colman A (2013) An approach to software reliability prediction based on time series modeling. J Syst Softw 86(7):1923–1932
Bao Y, Sun X, Trivedi K (2005) A workload-based analysis of software aging, and rejuvenation. Reliab IEEE Trans 54(3):192–206
Chatterjee S, Nigam S, Singh JB, Upadhyaya LN (2012) Software fault prediction using nonlinear autoregressive with eXogenous Inputs (NARX) network. Appl Intell 37(1):121–129
Chen K-Y, Wang C-H (2007) A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Syst Appl 32:254–264
Choras M, Kozik R, Pawlicki M, Holubowicz W, Franch X (2019) Software development metrics prediction using time series methods. Computer information systems and industrial management. Springer, Cham, pp 311–323
Davies R, Coole T, Osipyw D (2014) The application of time series modelling and Monte Carlo simulation: forecasting volatile inventory requirements. Appl Math 5(8):1152–1168
Fan Q, Fan H (2015) Reliability analysis and failure prediction of construction equipment with time series models. J Adv Manag Sci 3(3):203–210
Grottke M, Li L, Vaidyanathan K, Trivedi KS (2006) Analysis of software aging in a web server. IEEE Trans Reliab 55(3):206–218
Gupta A, Mohan BR, Sharma S, Agarwal R, Kavya K (2013) Prediction of software anomalies using time series analysis—a recent study. Int J Adv Comput Theory Eng 2(3):101–108
Ho S, Xie M (1998) The use of ARIMA models for reliability forecasting and analysis. Comput Ind Eng 35:213–216
Huang C, Lyu MR (2011) Estimation and analysis of some generalized multiple change-point software reliability models. IEEE Trans Reliab 60(2):498–514
Jang S-B, Kim Y-G, Lee SK (2011) Variability management for software product-line architecture development. Int J Software Eng Knowl Eng 21(7):931–956
Karunanithi N, Whitley D, Malaiya YK (1992) Prediction of software reliability using connectionist models. IEEE Trans Softw Eng 18(7):563–573
Musa JD (1985) John D. Musa on software: productivity, quality, and human factors. IEEE Spectr 22(1):37–37
Pai P-F, Lin C-S (2003) A hybrid ARIMA and support vector machines model in stock price forecasting. Int J Manag Sci 33:497–505
Pati J, Shukla KK (2015) A hybrid technique for software reliability prediction. ISEC 15:139–146
Rao TS, Sabr MM (1984) An introduction to bispectral analysis and bilinear time series models. Lecture notes in statistics vol 24. Springer, New York
Robinson D, Dietrich D (1987) A new nonparametric growth model. IEEE Trans Reliab 36(4):411–418
Sampathkumar A, Vivekanandan P (2019) Gene selection using PLOA method in microarray data for cancer classification. J Med Imaging Health Inform 9(6):1294–1300
Sampathkumar A, Rastogi R, Arukonda S, Shankar A, Kautish S, Sivaram M (2020) An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01731-7
Sharda R, Patil RB (1990) Neural networks as forecasting experts: an empirical test. Proc Int Jt Conf Neural Netw Wash 2:491–494
Singpurwalla N, Soyer R (1985) Assessing (Software) reliability growth using a random coefficient autoregressive process and its ramifications. IEEE Trans Softw Eng 11:1456–1464
Subramanian V, Hung MS (1993) A GRG2-based system for training neural networks: design and computational experience. ORSA J Comput 5:386–394
Tang Z, Almeida C, Fishwick PA (1991) Time series forecasting using neural networks vs Box–Jenkins methodology. Simulation 57:303–310
Tran VG, Debusschere V, Bacha S (2012) Hourly server workload forecasting up to 168 hours ahead using seasonal ARIMA model. In: 13th international conference industrial technology, pp 1127–1131
Wiper M, Palacios A, Marín J (2012) Bayesian software reliability prediction using software metrics information. Qual Technol Quant Manag 9:35–44
Yang B, Li X, Xie M, Tan F (2010) A generic data-driven software reliability model with model mining technique. Reliab Eng Syst Saf 95:671–678
Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kumaresan, K., Ganeshkumar, P. Software reliability prediction model with realistic assumption using time series (S)ARIMA model. J Ambient Intell Human Comput 11, 5561–5568 (2020). https://doi.org/10.1007/s12652-020-01912-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-01912-4