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Home»Articles»Smart Prediction Method of Software Defect Using Neuro-Fuzzy Approach

JournalCover

Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Smart Prediction Method of Software Defect Using Neuro-Fuzzy Approach

Author : Sunil Kumar Singh and Raj Shree
Volume 7 No.2 July-September 2018 pp 6-10

Abstract

Faults in software program structures continue to be a primary problem. A software fault is a disorder that reasons software failure in an executable product. A form of software fault predictions techniques were proposed, however none has proven to be continually correct. So, on this examine the overall performance of the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting software program defects and software program reliability has been reviewed. The datasets are taken from NASA Metrics Data Program (MDP) statistics repository. In the existing work a synthetic intelligence technique viz. Adaptive Neuro Fuzzy Inference System (ANFIS) goes for use for software disorder prediction.

Keywords

Neuro-Fuzzy Approach, Smart Prediction, Software, Defect

Full Text:

References

[1] Parvinder S. Sandhu, Sunil Khullar, Satpreet Singh, Simranjit K. Bains, Manpreet Kaur and Gurvinder Singh, “A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm”, World Academy of Science, Engineering and Technology, 2010, pp. 648-653.
[2] S. Bibi, G. Tsoumakas, I. Stamelos and I. Vlahavas, “Software Defect Prediction Using Regression via Classification”, IEEE International Conference on Computer Systems and Applications, March 8, pp. 330 – 336, 2006.
[3] Norman Fenton, Paul Krause and Martin Neil, “A Probabilistic Model for Software Defect Prediction”, IEEE Transactions in Software Engineering.
[4] Ahmet Okutan and Olcay Taner Yıldız, “Software defect prediction using Bayesian networks”, Empir Software Eng (2014) 19:154–181 © Springer Science+Business Media, LLC, 2012.
[5] Mrinal Singh Rawat and Sanjay Kumar Dubey, “Software Defect Prediction Models for Quality Improvement: A Literature Study”, IJCSI International Journal of Computer Science Issues, Vol. 9, No. 2, Issue 5, 2012, pp. 288-296.
[6] Supreet Kaur and Dinesh Kumar, “Software Fault Prediction in Object Oriented Software Systems Using Density Based Clustering Approach”, International Journal of Research in Engineering and Technology (IJRET), Vol. 1, No. 2 March, 2012, ISSN: 2277-4378.

7] Xiao-dong Mu, Rui-hua Chang and Li Zhang, “Software Defect Prediction Based on Competitive Organization CoEvolutionary Algorithm”, Journal of Convergence Information Technology (JCIT), Vol. 7, No. 5, 2012.
[8] N Fenton, M Neil and D Marquez, “Using Bayesian networks to predict software defects and reliability”, Review Paper, Proc. IMechE, Vol. 222, Part O: J. Risk and Reliability, 2008.
[9] Jie Xu, Danny Ho and Luiz Fernando Capretz, “An Empirical Study on the Procedure to Derive Software Quality Estimation Models”, International Journal of Computer Science and Information Technology (IJCSIT), Vol. 2, No. 4, 2010.
[10] Manu Banga, “Computational Hybrids towards Software Defect Predictions”, International Journal of Scientific Engineering and Technology, Vol. 2, No. 5, pp. 311-316, 2013.
[11] J. S. R. Jang, “ANFIS-Adaptive-Network Based Fuzzy Inference System”, IEEE Transactions on Systems, Man and Cybernatics, Vol. 23, No. 3, pp. 665-685, 1993.
[12] Prabhishek Singh and Raj Shree, “Quantitative Dual Nature Analysis of Mean Square Error in SAR Image Despeckling, International Journal on Computer Science and Engineering (IJCSE), Vol. 9, No. 11, pp. 619-622, Nov. 2017.
[13] Prabhishek Singh, Raj Shree, “A Comparative Study to Noise Models and Image Restoration Techniques”, International Journal of Computer Applications (0975 – 8887), Vol. 149, No. 1, September 2016.
[14] Prabhishek Singh, Raj Shree, “Statistical Quality Analysis of Wavelet Based SAR Images in Despeckling Process”, Asian Journal of Electrical Sciences (AJES), Vol. 6, No. 2, pp. 1-18, July-December 2017.
[15] Prabhishek Singh and Raj Shree, “A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion”, Journal of King Saud University – Computer and Information Sciences, 2017, http://dx.doi.org/10.1016/j.jksuci.2017.
06.006
[16] Prabhishek Singh, Raj Shree and Manoj Diwakar, “A new SAR image despeckling using correlation based fusion and method noise thresholding”, Journal of King Saud University – Computer and Information Sciences, 2018, https://doi.org/10.1016/j.jksuci.2018.
03.009
[17] Prabhishek Singh and Raj Shree, “Statistical Modelling of Log Transformed Speckled Image”, International Journal of Computer Science and Information Security, Vol. 14, No. 8, pp. 426-431, August 2016.
[18] Prabhishek Singh and Raj Shree, “A New Computationally Improved Homomorphic Despeckling Technique of SAR Images”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 3, March April 2017.
[19] Prabhishek Singh and Raj Shree, “Analysis and effects of speckle noise in SAR images”, IEEE International Conference on Advances in Computing, Communication, Automation (ICACCA), 2016.
[20] Prabhishek Singh and Raj Shree, “A new SAR image despeckling using directional smoothing filter and method noise thresholding”, Eng. Sci. Tech., Int. J., 2018, https://doi.org/10.1016/j.jestch.2018.
05.009.
[21] Kavita Sahu, R Shree, R Kumar, “Risk Management Perspective in SDLC”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, No. 3, 2014.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

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Faults in software program structures continue to be a primary problem. A software fault is a disorder that reasons software failure in an executable product. A form of software fault predictions techniques were proposed, however none has proven to be continually correct. So, on this examine the overall performance of the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting software program defects and software program reliability has been reviewed. The datasets are taken from NASA Metrics Data Program (MDP) statistics repository. In the existing work a synthetic intelligence technique viz. Adaptive Neuro Fuzzy Inference System (ANFIS) goes for use for software disorder prediction.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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