Taxonomy of intelligence software reliability model

  • Authors

    • Saeed Ahmadluei Islamic Azad University, Qazvin Branch, Qazvin, Iran
    2015-02-16
    https://doi.org/10.14419/jacst.v4i1.4211
  • Genetic Algorithm, Intelligence System, Neural Network, Software Engineering, Software Reliability.

  • Abstract

    The probability of failure free software operation for a specified period of time in a specified environment is called Reliability, it is one of the attributes of software quality and study about it come back to 1384. Exposition and spreading of new software systems and profound effect of it to human life emphasize the importance of software reliability analysis, until it poses formal definition at 1975. First race of reliability analysis methods that we called classic methods has stochastic process approach and in this way, attempt to predict the software behavior in future. Due to the ambiguity in fruitfulness of these solutions the challenge about reliability analysis continued till now. Great tendency in applying intelligence systems at variety of applications can be seen at 90 decade, and software reliability attracts some research direction to itself. Until now variety of methods in reliability analysis on the base of intelligence systems approach exhibited. In this survey the taxonomy of these methods represented with brief description of each one. Also comparison between these methods can be seen at the end of survey.

  • References

    1. [1] Dionysius lardner “Babbage s’ calculating engine†computer society, 1834.

      [2] Johon D. Musa “A Theory of Software Reliability and its Application†IEEE Trans. Softw. Eng., vol. SE-1, no.3, pp. 312–327, Sep. 1975 http://dx.doi.org/10.1109/TSE.1975.6312856.

      [3] N.Karunanithi, D. Whitley “Prediction of Software Reliability Using Connectionist Models†IEEE Trans. Softw. Eng., vol.18, no.7, pp. 563–574, Jul. 1992 http://dx.doi.org/10.1109/32.148475.

      [4] J.P. Carnegie Mellon university http://www.ece.cmu.edu/~koopman/des_s99/sw_reliability/

      [5] M.Xie,Y.S.Dai, K.L.Poh “Computing System Reliability Models and Analysis†Kluwer AcademicPublishers – 2004

      [6] Lai, C.D., Xie, M., Poh, K.L., Dai, Y.S. and Yang, P. “A model for availability analysis of distributed software/hardware systems†Information and Software Technology, 44 (6), 343-350. 2002 http://dx.doi.org/10.1016/S0950-5849(02)00007-1.

      [7] R.Presman “Software Engineering A Practitioner`s Approach†7edition, Mc Graw Hill, 2010

      [8] E.O.Costa,A.T.R.Pozo “A Genetic Programming Approach for Software Reliability Modeling†IEEE Trans. Reliability, vol.59, no.1,pp. 222–230, Mar. 2010 http://dx.doi.org/10.1109/TR.2010.2040759.

      [9] K.Y. Cai, L. Cai, W.D. Wang, Z.Y. Yu, D. Zhang, “On the neural network approach in software reliability modeling†The Journal of Systems and Software vol.58, 2001, pp. 47–62 http://dx.doi.org/10.1016/S0164-1212(01)00027-9.

      [10] T.Dohi, Y.Nishio, S.Osaki, “Optional software release scheduling based on artificial neural networksâ€Annals of Software Engineering vol.8, 1999, pp.167–185 http://dx.doi.org/10.1023/A:1018962910992.

      [11] N.Karunanithi, D.Whitley, Y.K.Malaiya, “Prediction of software reliability using neural networks†International Symposium on Software Reliability, 1991, pp.124–130.

      [12] T.M.Khoshgoftaar, R.M.Szabo “Predicting software quality, during testing using neural network models:a comparative study†International Journal of Reliability, Quality and Safety Engineering 1,1994, pp.303–319 http://dx.doi.org/10.1142/S0218539394000222.

      [13] T.M.Khoshgoftaar, E.B.Allen, W.D.Jones, “Classification – tree models of software quality over multiple releases†IEEE Transactions on Reliability vol.49, 2000, pp.4–11 http://dx.doi.org/10.1109/24.855532.

      [14] N.Karunanithi, Y.K.Malaiya, “The scaling problem in neural networks for software reliability prediction†Proceedings of the 3rd International IEEE Symposium of Software Reliability Engineering, Los Alamitos, CA, 1992, pp.76–82 http://dx.doi.org/10.1109/ISSRE.1992.285856.

      [15] N.Karunanithi, D.Whitley “Prediction of software reliability using connectionist models†IEEE Transactions on Software Engineering vol.18, 1992, pp.563–574 http://dx.doi.org/10.1109/32.148475.

      [16] S.H.Aljahdali,K.A.Buragga “Employing four ANNs Paradigms for Software Reliability Prediction: an Analytical Study†ICGST-AIML Journal, ISSN: 1687-4846, Vol.8, No.2, Sep 2008.

      [17] Aljahdali, S., Sheta, A., and Rine, D., “Predicting Accumulated Faults in Software Using Radial Basis Function Network†Proceedings of the ISCA 17th International Conference on Computers and their Application, 4-6, April 2002, pp. 26-29.

      [18] Y.Wu, R.Yang “Study of Software Reliability Prediction Based on GR Neural Network†School of Reliability and Systems Engineering Beihang University, DOI: 978-1-61284-666-8 IEEE 2010.

      [19] J. L. Elman “Finding structure in time†Cognitive Science, pp. 179-211, 1990 http://dx.doi.org/10.1207/s15516709cog1402_1.

      [20] M. I. Jordan, “Attractor dynamics and parallelism in a connectionist sequential machine,†Proc. 8th Annual Conf. Cognitive Science, pp.531-546, 1986

      [21] R. I. Williams,D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,†Neural Computation, vol. 1, pp. 270-280, 1989 http://dx.doi.org/10.1162/neco.1989.1.2.270.

      [22] K.Y.Cai,L.CaiW.D.Wang “On the neural network approach in software reliability“ Journal of system and software(elseavier), vol.58, pp 47-62 ,2001 http://dx.doi.org/10.1016/S0164-1212(01)00027-9.

      [23] N. Karunanithi, Y.K. Malaiya, D. Whitley “The scaling problem in neural networks for software reliability prediction†Proceedings of the Third International IEEE Symposium of Software Reliability Engineering, Los Alamitos, CA, 1992, pp. 76–82 http://dx.doi.org/10.1109/ISSRE.1992.285856.

      [24] N. Karunanithi, D. Whitley, Y.K. Malaiya, “Prediction of software reliability using connectionist models†IEEE Tran. on Software Engineering vol.18, 1992, pp 563–574 http://dx.doi.org/10.1109/32.148475.

      [25] R. Sitte “Comparison of software-reliability-growth predictions: neural networks vs parametric-recalibration†IEEE Tran. on Reliability vol.48 NO.3, 1999, pp. 285–291 http://dx.doi.org/10.1109/24.799900.

      [26] L. Tian, A. Noore, “On-line prediction of software reliability using an evolutionary connectionist model†The Journal of Systems and Software, vol.77, 2005, pp.173–180 http://dx.doi.org/10.1016/j.jss.2004.08.023.

      [27] Y.S.Su,C.Y.Huang “Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models†Journal of Systems and Software(elseavier),vol.80, pp. 606–615, 2007 http://dx.doi.org/10.1016/j.jss.2006.06.017.

      [28] T.M. Khoshgoftaar, R.M.Szabo “Using Neural Networks to Predict Software Faults During Testing†IEEE Trans. Reliability., vol. 45, no.3,pp. 456–462, Sep. 1996 http://dx.doi.org/10.1109/24.537016.

      [29] John R. Koza “Genetic Programming†MIT Press 1998.

      [30] E.O.Costa, A.T.R.Pozo “A Genetic Programming Approach for Software Reliability Modeling†IEEE Trans.Reliability., vol. 59, no.1,pp. 222–230, Mar. 2010 http://dx.doi.org/10.1109/TR.2010.2040759.

      [31] V.Vapnik “The Nature of Statistical Learning Theory†Springer,New York, 1995 http://dx.doi.org/10.1007/978-1-4757-2440-0.

      [32] A.J.Smola, B.S.Lkopf “A tutorial on support vector regressionâ€, Statistics and Computing, vol.14,pp.199–222, Nov 2004 http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88.

      [33] B.Yang, X.Li “A Study on Software Reliability Prediction Based on Support Vector Machines†University of Electronic Science and Technology of China, IEEE, doi : 1-4244-1529-2/07, 2007

      [34] A. Wood, “Predicting software reliability,†Computer, vol.29, no. 11, pp. 69-77, 1996 http://dx.doi.org/10.1109/2.544240.

      [35] P.F.Pai, W.C.Hong “Software reliability forecasting by support vector machines with simulated annealing algorithms†The Journal of Systems and Software vol.79, pp.747–755, 2006 http://dx.doi.org/10.1016/j.jss.2005.02.025.

      [36] H.Can1, X.Jianchun1, Z.Ruide â€A New Model for Software Defect Prediction Using Particle Swarm Optimization and Support Vector Machine“ University of Science and Technology, Nanjing, China, DOI : 978-1-4673-5534-6/13, IEEE,2013

      [37] Z.Qiuhong “Research of Software Failure Prediction Based on Support Vector Regression†The 2nd International Conference on Computer Application and System Modeling, 2012

      [38] C.Jin “Software reliability prediction based on support vector regression using a hybrid genetic algorithm and simulated annealing algorithm†IET Software, Vol. 5, Iss. 4, pp.398–405, 2011 http://dx.doi.org/10.1049/iet-sen.2010.0073.

      [39] L.Tian, A.Noore “Evolutionary neural network modeling for software cumulative failure time prediction†Reliability Engineering and System Safety(elsevier), vol.87, pp 45–51, 2005 http://dx.doi.org/10.1016/j.ress.2004.03.028.

      [40] Osaki, Shunji “Software Reliability Models†Book Section, Stochastic Models in Reliability and Maintenance,pp 53-280, Springer Berlin Heidelberg, P.B.2002

      [41] Jelinski, Z. and Moranda “Software reliability researchâ€, In: Freiberger W. (ed), Statistical Computer Performance Evaluation, New York: Academic Press, pp.465-497. P.B. 1972

      [42] P. K. Kapur “Artificial Neural Networks Based SRGM†Springer Series in Reliability Engineering“Software Reliability Assessment with OR Applicationsâ€, DOI: 10.1007/978-0-85729-204-9_7, 2011 http://dx.doi.org/10.1007/978-0-85729-204-9_7.

      [43] Iyer, R.K, Lee, “Measurement Based analysis of software reliability†In: Handbook of software reliability engineering McGraw-Hill, New York, pp 303-358, 1996

      [44] Musa, J.D., Iannino, A., Okumoto, K. â€Software Reliability,Measurement, Prediction and application†McGraw-Hill, New York, 1987

      [45] G.Paris, D.Robilliard,C.Fonlupt “Applying Boosting Techniques to Genetic Programming†Universit´e du Littoral-Cˆote d’Opale,Springer-Verlag Berlin Heidelberg pp. 267–278, 2002

      [46] S.Yamada “Software Reliability Models†Tottori University,Book chapter,Springer-Verlag Berlin Heidelberg, 2002

      [47] R.H.Reussner, H.W.Schmidt “Reliability prediction for component-based software architectures†Journal of Systems and Software, vol.66, pp. 241–252, 2003 http://dx.doi.org/10.1016/S0164-1212(02)00080-8.

      [48] R. Chillarege, “Orthogonal defect classification†in Handbook of Software Reliability Engineering, M. R. Lyu, Ed. New York: McGraw-Hill,1996, pp. 359–400

      [49] S.Dick, C.L. Bethel, A.Kandel, “Software-Reliability Modeling: The Case for Deterministic Behavior†IEEE Trans. On Systems man and cyber, vol. 37, no.1,pp. 106–119, Janu,2007

      [50] A.L.Goel, K.Okumoto “GoelOkumoto Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures†IEEE Trans. Reliability., vol. 28, no.3,pp. 206–212, Aug. 1979 http://dx.doi.org/10.1109/TR.1979.5220566.

      [51] Osaki, Shunji “Software Reliability Models†Book Section of Stochastic Models in Reliability and Maintenance,pp 53-280, Springer Berlin Heidelberg, P.B.2002.

  • Downloads

  • How to Cite

    Ahmadluei, S. (2015). Taxonomy of intelligence software reliability model. Journal of Advanced Computer Science & Technology (JACST), 4(1), 60-67. https://doi.org/10.14419/jacst.v4i1.4211

    Received date: 2015-01-19

    Accepted date: 2015-02-10

    Published date: 2015-02-16