Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


A popular theory and explanation of the contemporary changes occurring around us is that we are in the midst of a third major revolution in human civilization, i.e., a Third Wave. First there was the Agricultural Revolution, then the Industrial Revolution, and now we are in the Information Revolution. Yet we are, in fact, in the middle of a revolutionary jump. Information and communication technology and a worldwide system of information exchange have been building growth for over a 100 years. Information technology (IT) is playing a crucial role in contemporary society. It has transformed the whole world into a global village with a global economy.


Software Development Software Reliability Reliability Function Software Development Process Reliability Growth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



  1. 1.
    Lyu MR (1996) Handbook of software reliability engineering. McGraw-Hill, New York ISBN 0-7-039400-8Google Scholar
  2. 2.
    Kapur PK, Garg RB, Kumar S (1999) Contributions to hardware and software reliability. World Scientific, SingaporeMATHCrossRefGoogle Scholar
  3. 3.
    Pham H (2006) System software reliability. Reliability engineering series. Springer, LondonGoogle Scholar
  4. 4.
    Garfinkel S (2005) History’s Worst Software Bugs
  5. 5.
    Musa JD (1998) Software reliability engineering. McGraw-Hill, New York ISBN 0-07-913271-5Google Scholar
  6. 6.
    Pressman RS (2005) Software engineering: a practitioner’s approach, 6th edn. Mc-Graw Hill Professional, NY ISBN 0-07-285318-2Google Scholar
  7. 7.
    Sommerville L (1995) Software engineering, 5th edn. Addison Wesley Longman Publishing Co., Inc, Redwood City ISBN:0-201-42765-6Google Scholar
  8. 8.
    Goel AL (1985) Software reliability models: assumptions, limitations and applicability. IEEE Trans Softw Eng SE-11:1411–1423CrossRefGoogle Scholar
  9. 9.
    Musa JD, Iannino A, Okumoto K (1987) Software reliability: measurement, prediction, application. McGraw-Hill, New York ISBN 0-07-044093-XGoogle Scholar
  10. 10.
    Ramamoorthy CV, Bastani FB (1982) Software reliability status and perspectives. IEEE Trans Reliability 37(1):88–91Google Scholar
  11. 11.
    Xie M (1990) Software reliability modeling. World Scientific Publications, SingaporeGoogle Scholar
  12. 12.
    Popstojanova K, Trivedi K (2001) Architecture based approach to reliability assessment of software systems. Performance Evaluation 45(2):179–204MATHCrossRefGoogle Scholar
  13. 13.
    Asad CA, Muhammad Ullah I, Muhammad Rehman J (2004) An approach for software reliability model selection. In: Proceedings 28th annual international computer software and applications conference (COMPSAC’04), pp 534–539Google Scholar
  14. 14.
    Goel AL, Okumoto K (1979) Time dependent error detection rate model for software reliability and other performance measures. IEEE Trans Reliability R-28(3):206–211CrossRefGoogle Scholar
  15. 15.
    Jelinski Z, Moranda P (1972) Software reliability research. In: Freiberger W (ed) Statistical computer performance evaluation. Academic Press, New York, pp 465–484Google Scholar
  16. 16.
    Schick GJ, Wolverton RW (1978) An analysis of competing software reliability models. IEEE Trans Softw Eng 4(2):104–120MATHCrossRefGoogle Scholar
  17. 17.
    Cheung RC (1980) A user oriented software reliability growth model. IEEE Trans Softw Eng SE-6:118–125CrossRefGoogle Scholar
  18. 18.
    Littlewood B (ed) (1987) Software reliability: achievement and assessment. Blackwell, OxfordGoogle Scholar
  19. 19.
    Littlewood B, Verrall JL (1973) A Bayesian reliability growth model for computer software. Appl Stat 22:332–346MathSciNetCrossRefGoogle Scholar
  20. 20.
    Littlewood B, Sofer A (1987) A Bayesian modification to the Jelinski–Moranda software reliability model. Softw Eng J 2(2):30–41CrossRefGoogle Scholar
  21. 21.
    Singpurwalla ND (1995) The failure rate of software: does it exist. IEEE Trans Reliability 44(3):463–469CrossRefGoogle Scholar
  22. 22.
    Singpurwalla ND, Wilson SP (1999) Statistical methods in software engineering, reliability and risk. Springer, New YorkMATHCrossRefGoogle Scholar
  23. 23.
    Schneidewind NF (1975) Analysis of error processes in computer software. Sigplan Not 10:337–346CrossRefGoogle Scholar
  24. 24.
    Gupta A (2009) Some contributions to modeling and optimization in software reliability and marketing. Ph.D. Thesis, Department of OR, Delhi University, DelhiGoogle Scholar
  25. 25.
    Yamada S, Nishigaki A, Kimura M (2003) A stochastic differential equation model for software reliability assessment and its goodness of fit. Int J Reliability Appl 4(1):1–11Google Scholar
  26. 26.
    Yamada S, Tamura Y (2006) A flexible stochastic differential equation model in distributed development environment. Eur J Oper Res 168:143–152MathSciNetMATHCrossRefGoogle Scholar
  27. 27.
    Kapur PK, Singh VB, Anand S (2007) Effect of change-point on software reliability growth models using stochastic differential equation. In: 3rd International conference on reliability and safety engineering (INCRESE-2007), Udaipur, 7–19 December 2007, pp 320–333Google Scholar
  28. 28.
    Su YS, Haung CY (2007) Neural network based approaches for software reliability estimation using dynamic weighted combinational models. J Syst Softw 80:606–615CrossRefGoogle Scholar
  29. 29.
    Kapur PK, Khatri SK, Goswami DN (2008) A generalized dynamic integrated software reliability growth model based on artificial neural network approach. Verma AK, Kapur PK, Ghadge SG (eds) Advances in performance and safety of complex systems. Macmillan advanced research series, pp 813–838Google Scholar
  30. 30.
    Kapur PK, Khatri SK, Basirzadeh M (2008) Software reliability assessment using artificial neural network based flexible model incorporating faults of different complexity. Int J Reliability Qual Safety Eng 15(2):113–127CrossRefGoogle Scholar
  31. 31.
    Kapur PK, Khatri SK, Yadav K (2008) An artificial neural-network based approach for developing a dynamic integrated software reliability growth model. Presented in international conference on present practices and future trends in quality and reliability, ICONQR08, 22–25 January 2008Google Scholar
  32. 32.
    Inoue S, Yamada S (2002) A software reliability growth modeling based on infinite server queuing theory. In: Proceedings 9th ISSAT international conference on reliability and quality in design, Honolulu, HI, pp 305–309Google Scholar
  33. 33.
    Dohi T, Osaki S, Trivedi KS (2004) An infinite server queuing approach for describing software reliability growth—unified modeling and estimation framework. In: Proceedings 11th Asia-Pacific software engineering conference (APSEC’04), pp 110–119Google Scholar
  34. 34.
    Kapur PK, Kumar J, Kumar R (2008) A unified modeling framework incorporating change point for measuring reliability growth during software testing. OPSEARCH J Oper Res Soc India 45(4):317–334Google Scholar
  35. 35.
    Kapur PK, Anand S, Inoue S, Yamada S (2010) A unified approach for developing software reliability growth model using infinite server queuing model. Int J Reliability Qual Safety Eng, to appearGoogle Scholar
  36. 36.
    Kapur PK, Pham H, Anand S, Yadav K (2011) A unified approach for developing software reliability growth models in the presence of imperfect debugging and error generation. IEEE Trans Softw Reliability, doi  10.1109/TR.2010.2103590 Google Scholar
  37. 37.
    Langberg N, Singpurwalla ND (1985) Unification of some software reliability models. SIAM J Comput 6:781–790MathSciNetMATHCrossRefGoogle Scholar
  38. 38.
    Kapur PK, Gupta A, Jha PC (2007) Reliability growth modeling and optimal release policy of a n-version programming system incorporating the effect of fault removal efficiency. Int J Autom Comput Springer 4(4):369–379CrossRefGoogle Scholar
  39. 39.
    Kapur PK, Gupta A, Gupta D, Jha PC (2008) Optimum software release policy under fuzzy environment for a n-version programming system using a discrete software reliability growth model incorporating the effect of fault removal efficiency. Verma AK, Kapur PK, Ghadge SG (eds) Advances in performance and safety of complex systems. Macmillan advance research series, pp 803–816Google Scholar
  40. 40.
    Putsis WP (1998) Parameter variation and new product diffusion. J Forecasting 17(3–4):231–257CrossRefGoogle Scholar
  41. 41.
    Hardie BGS, Fader PS, Wisniewski M (1998) An empirical comparison of new product trial forecasting models. J Forecasting 17:209–229CrossRefGoogle Scholar
  42. 42.
    Schmittlein DC, Mahajan V (1982) Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Sci 1(1):57–78CrossRefGoogle Scholar
  43. 43.
    Meade N, Islamb T (2006) Modeling and forecasting the diffusion of innovation—a 25 year review. Int J Forecast 22(3):519–545CrossRefGoogle Scholar
  44. 44.
    Chen X, Ender P, Mitchell M, Wells C (2003) Regression with SPSS. Accessed 13 July 2010
  45. 45.
    Garson GD (2009) Nonlinear regression. Accessed 13 July 2010
  46. 46.
    Abramson MA, Chrissis JW (1998) Sequential quadratic programming and the ASTROS structural optimization system. Struct Optim 15:24–32CrossRefGoogle Scholar
  47. 47.
    Gill PR, Murray W, Wright MH (1981) Practical optimization. Academic Press, LondonMATHGoogle Scholar
  48. 48.
    Madsen K, Nielsen KB, Tingleff O (2004) IMM methods for non-linear least squares problems. Informatics and Mathematical Modeling, Technical University of DenmarkGoogle Scholar
  49. 49.
    Pillai EEK, Nair VSS (1997) A model for software development effort and cost estimation. IEEE Trans Softw Eng 23(8):485–497CrossRefGoogle Scholar
  50. 50.
    Musa JD, Iannino A, Okumoto K (1989) Software reliability: measurement, prediction, application. McGraw-Hill, New York. ISBN 0-07-044093-XGoogle Scholar

Copyright information

©  Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Operational ResearchUniversity of DelhiDelhiIndia
  2. 2.Department of Industrial and Systems EngineeringRutgers UniversityPiscatawayUSA

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