Abstract
Due to demand of new features and highly reliable software system, the software industries are speeding their up-gradations/add-ons in the software. The life of software is very short in the environment of perfect competition. Therefore the software developers have to come up with successive up gradations to survive. The reported bugs from the existing software and Features added to the software at frequent time intervals lead to complexity in the software system and add to the number of faults in the software. The developer of the software can lose on market share if it neglects the reported bugs and up gradation in the software and on the other hand a software company can lose its name and goodwill in the market if the reported bugs and functionalities added to the software lead to an increase in the number of faults in the software. To capture the effect of faults due to existing software and generated in the software due to add-ons at various points in time, we develop a multi up-gradation, multi release software reliability model. This model uniquely identifies the faults left in the software when it is in operational phase during the testing of the new code i.e. developed while adding new features to the existing software. Due to complexity and incomplete understanding of the software, the testing team may not be able to remove/correct the fault perfectly on observation/detection of a failure and the original fault may remain resulting in the phenomenon known as imperfect debugging, or get replaced by another fault causing error generation The model developed is validated on real data sets with software which has been released in the market with new features four times.
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References
Bittanti S, Bolzern P, Pedrotti E, Scattolini R (1998) A flexible modeling approach for software reliability growth. In: Goos G, Harmanis J (eds) Software reliability modelling and identification. Springer, Berlin, pp 101–140
Goel AL (1985) Software reliability models: assumptions, limitations and applicability. IEEE Trans Softw Eng SE-11:1411–1423
Goel L, Okumoto K (1979) Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Trans Reliab 28(3):206–211
Kanoun K, Bastos Martini M, Moreira De Souza J (1991) A method for software reliability analysis and prediction application to the TROPICO-R switching system. IEEE Trans Softw Eng 17(4):334–344
Kapur PK, Garg RB (1992) Software reliability growth model for an error-removal phenomenon. Softw Eng J 7(4):291–294
Kapur PK, Younes S, Agarwala S (1995) Generalized Erlang model with n types of faults. ASOR Bull 14(1):5–11
Kapur PK, Garg RB, Kumar S (1999) Contributions to hardware and software reliability. World Scientific, Singapore
Kapur PK, Singh VB, Yang B (2007) Software reliability growth model for determining fault types. In: Proceedings of the 3rd international conference on reliability and safety engineering (INCRESE ‘07), Dec 2007. Reliability Center, Kharagpur, India, pp 334–349
Kapur PK, Anshu G, Jha PC, Goyal SK (2010) Software quality assurance using software reliability growth modeling. Int J Bus Inf Syst (IJBIS) 6(4):463–496
Kapur PK, Tandon A, Kaur G (2010) Multi up-gradation software reliability model. Published in the proceedings of 2nd International conference on reliability, safety & hazard (ICRESH-2010), pp 468–474
Khataneh K, Mustafa T (2009) Software reliability modeling using soft computing technique. Eur J Sci Res 26(1):154–160
Lin C-T, Huang C-Y (2008) Enhancing and measuring the predictive capabilities of testing-effort dependent software reliability models. J Syst Softw 81:1025–1038
Luo Y, Bergander T, Hamza A (2001) Software reliability growth modeling using a weighed Laplace test statistic. 31st Annual international Computer Software and Applications Conference (COMPSAC 2007), 2:305–312
Ohba M (1984) Software reliability analysis models. IBM J Res Dev 28(4):428–443
Pham H (2006) System software reliability. Springer-Verlag, London
Pham H, Zhang X (2003) NHPP software reliability and cost models with testing coverage. Eur J Oper Res 145:443–454
Williams M (2005) Software glitch halts Tokyo Stock Exchange. InfoWorld. http://www.infoworld.com/article/05/11/01/ HNtokyoexchange_1.html?APPLICATION%20PERFORMANCE%20MANAGEMENT. Accessed 30 Jul 2008. Associated Press (20 Apr 2006). Official: software glitch, not bomb, shut airport. MSNBC. http://www.msnbc.msn.com/id/12411853/. Accessed 30 Jul 2008
Yamada S, Ohba M, Osaki S (1984) S-shaped software reliability growth models and their applications. IEEE Trans Reliab 33(4):289–292
Yamada S, Osaki S, Tanio Y (1992) Software reliability measurement and assessment methods during operation phase and their comparisons. Syst Comput Jpn 23(7):23–34
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Kapur, P.K., Singh, O., Garmabaki, A.S. et al. Multi up-gradation software reliability growth model with imperfect debugging. Int J Syst Assur Eng Manag 1, 299–306 (2010). https://doi.org/10.1007/s13198-011-0031-3
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DOI: https://doi.org/10.1007/s13198-011-0031-3