Software reliability forecasting by support vector machines with simulated annealing algorithms

Journal of Systems and Software

Volumn 79, Issue 6, 2006, Pages 747-755

  Pai, Ping Feng ^a   Hong, Wei Chiang ^b  

^a NATIONAL CHI NAN UNIVERSITY   (Taiwan)

^b DA YEH UNIVERSITY   (Taiwan)

Author keywords

Simulated annealing algorithms; Software reliability forecasting; Support vector machines

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; FORECASTING; RELIABILITY THEORY; SIMULATED ANNEALING;

SIMULATED ANNEALING ALGORITHMS; SOFTWARE RELIABILITY FORECASTING; SUPPORT VECTOR MACHINES;

COMPUTER SOFTWARE;

EID: 33646130599     PISSN: 01641212     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jss.2005.02.025     Document Type: Article

References (37)

1. Amjady N., and Ehsan M. Evaluation of power systems reliability by artificial neural network. IEEE Transaction on Power Systems 14 (1999) 287-292
2. Brocklehurst S., Chan P.Y., Littlewood B., and Snell J. Recalibrating software reliability models. IEEE Transactions on Software Engineering 16 (1990) 458-470
3. Burtschy B., Albeanu G., Boros D.N., Popentiu F., and Nicola V. Improving software reliability forecasting. Microelectronics and Reliability 37 (1997) 901-907
4. Cai K.Y. Software defect and operational profile modeling (1998), Kluwer Academic Publishers, Dordretch
5. Cai K.Y., Cai L., Wang W.D., Yu Z.Y., and Zhang D. On the neural network approach in software reliability modeling. The Journal of Systems and Software 58 (2001) 47-62
6. Cao L. Support vector machines experts for time series forecasting. Neurocomputing 51 (2003) 321-339
7. Cao L., and Gu Q. Dynamic support vector machines for non-stationary time series forecasting. Intelligent Data Analysis 6 (2002) 67-83
8. Dekkers A., and Aarts E.H.L. Global optimization and simulated annealing. Mathematical Programming 50 (1991) 367-393
9. El-Aroui M.A., and Soler J.L. A Bayes nonparametric framework for software-reliability analysis. IEEE Transactions on Reliability 45 (1996) 652-660
10. Ho S.L., Xie M., and Goh T.N. A comparative study of neural network and Box-Jenkins ARIMA modeling in time series forecasting. Computers and Industrial Engineering 42 (2002) 371-375
11. Kimura M., Yamada S., and Osaki S. Statistical software reliability prediction and its applicability based on mean time between failures. Mathematical and Computer Modeling 22 (1995) 149-155
12. Kirkpatrick S., Gelatt C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 (1983) 671-680
13. Liu M.C., Kuo W., and Sastri T. An exploratory study of a neural network approach for reliability data analysis. Quality and Reliability Engineering International 11 (1995) 107-112
14. Lyu M.R. Handbook of software reliability engineering (1996), McGraw-Hill, New York
15. Mercer J. Function of positive and negative type and their connection with the theory of integral equations. Philosophical Transaction Royal Society London A 209 (1909) 415-446
16. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., and Teller A.H. Equations of state calculations by fast computing machines. Journal of Chemical Physics 21 (1953) 1087-1092
17. Mohandes M.A., Halawani T.O., Rehman S., and Hussain A.A. Support vector machines for wind speed prediction. Renewable Energy 29 (2004) 939-947
18. Musa J.D. Software reliability data. IEEE Computer Society-Repository (1979)
19. Musa J.D., Jannino A., and Okumoto K. Software reliability measurement, prediction, application (1987), McGraw-Hill, New York
20. Myers G.J. Software reliability: Principles and practices (1976), John Wiley & Sons, New York
21. Pai, P.F., Lin, C.S. Using support vector machines in forecasting production values of machinery industry in Taiwan. International Journal of Advanced Manufacturing Technology, in press a.
22. Pai, P.F., Lin, C.S. A hybrid ARIMA and support vector machines model in stock price forecasting. OMEGA-International Journal of Management Science, in press b.
23. Pham L., and Pham H. Software reliability models with time-dependent hazard function based on Bayesian approach. IEEE Transactions on Systems, Man, and Cybernetics Part A 30 (2000) 25-35
24. Pham L., and Pham H. A Bayesian predictive software reliability model with pseudo-failures. IEEE Transactions on Systems, Man, and Cybernetics Part A 31 (2001) 233-238
25. Pham H., and Zhang X. NHPP software reliability and cost models with testing coverage. European Journal of Operational Research 145 (2003) 443-454
26. Singpurwalla N.D., and Soyer R. Assessing (software) reliability growth using a random coefficient autoregressive process and its ramifications. IEEE Transactions on Software Engineering SE-11 (1985) 1456-1464
27. Sitte R. Comparison of software-reliability-growth predictions: Neural networks vs parametric recalibration. IEEE Transactions on Reliability 48 (1999) 285-291
28. Su C.T., Tong L.I., and Leou C.M. Combining time series and neural network approaches. Journal of the Chinese Institute of Industrial Engineers 4 (1997) 419-430
29. Tay F.E.H., and Cao L. Application of support vector machines in financial time series forecasting. Omega: The International Journal of Management Science 29 (2001) 309-317
30. Tay F.E.H., and Cao L. Modified support vector machines in financial time series forecasting. Neurocomputing 48 (2002) 847-861
31. Van Laarhoven P.J.M., and Aarts E.H.L. Simulated annealing: Theory and applications (1987), Kluwer Academic Publishers, Dordrecht
32. Vapnik V. The nature of statistical learning theory (1995), Springer-Verlag, New York
33. Vapnik V. Statistical learning theory (1998), John Wiley and Sons, New York
34. Vojislav K. Learning and soft computing-support vector machines, neural networks and fuzzy logic models (2001), The MIT Press, Massachusetts
35. Wang W., Xu Z., and Lu J.W. Three improved neural network models for air quality forecasting. Engineering Computations 20 (2003) 192-210
36. Xie M. Software reliability modeling (1991), World Scientific, Singapore
37. Xu K., Xie M., Tang L.C., and Ho S.L. Application of neural networks in forecasting engine system reliability. Applied Soft Computing 2 (2003) 255-268