Recent soft computing methods in software reliability engineering

• Autorzy: Albeanu G. , Duda G.
• Języki publikacji: EN

Abstrakty

EN

This paper presents recent approaches in soft computing to manage imprecision and uncertainty which appear in software reliability engineering. Firstly, recent approaches like imprecise probabilities, generalized intervals, fuzzy sets and intuitionistic-fuzzy sets are shortly described, and the usage of intuitionistic fuzzy numbers for system reliability computation is shown. Intuitionistic-fuzzy approaches for software reliability growth models are proposed and experimental results are given.

Słowa kluczowe

EN

fuzzy sets; intuitionistic fuzzy sets; intuitionistic fuzzy numbers; subjective data analysis

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2011
• Tom: Vol. 2, No. 1
• Strony: 1--10
• Opis fizyczny: Bibliogr. 34 poz., tab.

Twórcy

autor

Albeanu G.

• Spiru Haret University, Bucharest, Romania

autor

Duda G.

• Spiru Haret University, Bucharest, Romania

Bibliografia

• [1] Albeanu, G. & Duda G. (2011). Intuitionistic Fuzzy Approaches for Quality Evaluation of Learning Objects. The 17th ISSAT International Conference on Reliability and Quality in Design -August 4-6, 2011 - Vancouver, B.C., Canada (in press).
• [2] Albeanu, G., Burtschy, B., Popentiu-Vladicescu, Fl. & Duda, G.I. (2010). Intuitionistic Fuzzy Methods in Optimal Software Reliability Allocation. In I. A. Ryabinin, E. D. Solojentsev (eds.), Modelling and Analysis of Safety and Risk in Complex Systems. Proc. of the International Scientific School MA SR. July 6-10, 2010, SaintPetersburg, Russia, pp. 206-212. Institute of Problems of Mechanical Engineering of Russian Academy of Sciences (IPME RAS).
• [3] Albeanu, G. (2010). On Some Recent Approaches to Imprecision and Uncertainty Modelling with Application to Complex Systems Reliability. OptimumQ, 21(4), 51-56.
• [4] Albeanu, G. (1998). Resampling Simultaneous Confidence Bands for Nonlinear Explicit Regression Models. Studii si Cercetari Matematice (Mathematical Reports), Romanian Academy Press, 50(5-6), 289-295.
• [5] Atanassov, K.T. (1999). Intuitionistic Fuzzy Sets. Physica-Verlag, Heidelberg, New York.
• [6] Cai, K.Y. (1996). Introduction to Fuzzy Reliability. Boston: Kluwer Academic Publishers.
• [7] Chen, C.H. (1985). Operations on fuzzy numbers with function principal. Tamkang J. Management Science, 6(1), 13-25.
• [8] Chen, S.-M. (2003). Analyzing Fuzzy System Reliability Using Vague Set Theory. International Journal of Applied Science and Engineering 1(1), 82-88.
• [9] Coolen, F.P.A. & Utkin, L.V. (2008). Imprecise reliability. In Melnick E.L., Everitt B.S. (eds.) Wiley Encyclopedia of Quantitative Risk Assessment, 2, 875-881, Wiley, 2008.
• [10] Coolen, F.P.A., Troffaes, M.C.M. & Augustin, T. (2010). Imprecise Probability. International Encyclopedia of Statistical Science, Springer.
• [11] De Cooman, G.D., Cozman, F.G., Moral, S. & Walley, P. (1999). Imprecise Probabilities Project, Gent.
• [12] Dubois, D. (2007). Uncertainty Theories: a Unified View. Cybernetic Systems Conference, Dublin (Ireland), 23-26/09/2007, IEEE, 4-9.
• [13] Jefrey, R. (2002). Subjective Probability. The Real Thing, http://www.princeton.edu/~bayesway /Book*.pdf.
• [14] Junhong, G., Xiaozong, Y. & Hongwei, L. (2005). Software Reliability Nonlinear Modeling and Its Fuzzy Evaluation, 4th WSEAS Int. conf. on non-linear analysis, non-linear systems and chaos, Sofia, Bulgaria, 49-54.
• [15] Khatatneh, K. & Mustafa, T. (2009). Software Reliability Modeling Using Soft Computing Technique. European Journal of Scientific Research, 26(1), 154-160.
• [16] Kiran, N.R. & Ravi, V. (2008). Software Reliability Prediction by Soft Computing Techniques. The Journal of Systems and Software, 81(4), 576-583.
• [17] Kumar A., Yadav, S.P. & Kumar, S. (2008). Fuzzy System Reliability using Different Types of Vague Sets. International Journal of Applied Science and Engineering 6(1), 71-83.
• [18] Lyu, M.R. (1996). Handbook of Software Reliability Engineering. IEEE Computer Society Press and McGraw-Hill.
• [19] Lyu, M.R. (2007). Software Reliability Engineering: A Roadmap. Proc. of FOSE 2007: Future of Software Engineering, 153-170.
• [20] Madsen, H., Thyregod, P., Burtschy, B., Popentiu, F. & Albeanu, G. (2005). On using soft computing techniques in software reliability engineering. Advances in Safety and Reliability – Kołowrocki (ed.), Taylor & Francis Group, London, 1317-1322.
• [21] Madsen, H., Thyregod, P., Burtschy, B., Albeanu, G. & Popentiu, F. (2006). On Using Soft Computing Techniques in Software Reliability Engineering. International Journal of Reliability, Quality, and Safety Engineering, 13(1), 61-72.
• [22] Mahapatra, G.S. & Roy, K.T. (2009). Reliability Evaluation using Triangular Intuitionistic Fuzzy Numbers Arithmetic Operations. International Journal of Computational and Mathematical Sciences, 3(5), 225-232.
• [23] Musa, J. D. (1999). Software Reliability Engineering. McGraw-Hill, New York.
• [24] Pfleiderer, J. & Krommidas. P. (1982). Statistics under incomplete knowledge of data. Royal Astron. Soc. Monthly Notices, 198, 281-288.
• [25] Popentiu, Fl. & Albeanu, G. (2003). On a Probabilistic-fuzzy Model in Software Reliability Engineering, Proc. of KONBIN' 2003 - The 3-rd Safety and Reliability International Conference: To Safer Life and Environment, Gdynia, Poland, May 26-30, 2003, Vol. 3, 517-523.
• [26] Singpurwalla, N.D. & Wilson, S.P. (1999). Statistical Methods in Software Engineering. Reliability and Risk. Springer Verlag.
• [27] Stringfellow, C. & Andrews A.A. (2002). An Empirical Method for Selecting Software Reliability Growth Models. Empirical Software Engineering, 7, 319-343.
• [28] Utkin, L.V. (2004). An uncertainty model of structural reliability with imprecise parameters of probability distributions. Zeitschrift für Angewandte Mathematik und Mechanik, 84(1011), 688-699.
• [29] Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. London: Chapman and Hall.
• [30] Wang, Y. (2010). Imprecise probabilities based on generalized intervals for system reliability assessment. Int. J. Reliability and Safety, 4(4): 319-342.
• [31] Wu, H-C. (2004). Fuzzy Reliability Estimation using Bayesian Approach. Computers & Industrial Engineering, 46, 467-493.
• [32] Yadav, O.P., Singh, N., Chinnam, R.B. & Goel, P.S. (2003). A Fuzzy Logic Based Approach to Reliability Improvement Estimation during Product Development. Reliability Engineering and System Safety, 80, 63-74.
• [33] Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
• [34] Zadeh, L.A. (2008). Computation with imprecise probabilities, In L. Magdalena, M. Ojeda-Aciego, J.L. Verdegay (eds.) Proc. of IPMU'08, Torremolinos (Málaga), June 22-27, 2008, http://www.gimac.uma.es/ipmu08/ proceedings/ papers/zadeh.pdf.