CFP last date
20 December 2024
Reseach Article

Software Reliability Prediction using Fuzzy Inference System: Early Stage Perspective

by Syed Wajahat A. Rizvi, Raees Ahmad Khan, Vivek Kumar Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 10
Year of Publication: 2016
Authors: Syed Wajahat A. Rizvi, Raees Ahmad Khan, Vivek Kumar Singh
10.5120/ijca2016910774

Syed Wajahat A. Rizvi, Raees Ahmad Khan, Vivek Kumar Singh . Software Reliability Prediction using Fuzzy Inference System: Early Stage Perspective. International Journal of Computer Applications. 145, 10 ( Jul 2016), 16-23. DOI=10.5120/ijca2016910774

@article{ 10.5120/ijca2016910774,
author = { Syed Wajahat A. Rizvi, Raees Ahmad Khan, Vivek Kumar Singh },
title = { Software Reliability Prediction using Fuzzy Inference System: Early Stage Perspective },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 10 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 16-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number10/25314-2016910774/ },
doi = { 10.5120/ijca2016910774 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:25.618386+05:30
%A Syed Wajahat A. Rizvi
%A Raees Ahmad Khan
%A Vivek Kumar Singh
%T Software Reliability Prediction using Fuzzy Inference System: Early Stage Perspective
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 10
%P 16-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper presents a reliability prediction model that predicts the reliability of the developing software using fuzzy inference system. The focus of the study is on the reliability prediction prior to the coding phase so that the developers use this information for optimally performing resource planning and quality assessment of the software under development. Requirements and object-oriented design level product measures have participated for early reliability prediction. The paper has also utilized the strengths of fuzzy logic to deal with the uncertainties and vagueness involved in the early stage measures. The model has also been statistically validated through the data set obtained through twenty real software projects. The values of the Pearson’s correlation coefficient along with the predictive accuracy measures are quite encouraging, and support that the developed model is a better and improved reliability prediction model.

References
  1. Reibman, A. L., and Veeraraghawan, M. 1991. Reliability Modeling: an overview for system design. IEEE Computer Sociaty, 24(4), 49-57.
  2. Lions, J. L. 2010. ARIANE 5 Flight - 501 Failures Report.
  3. Lyu, M. R. 1996. Handbook of Software Reliability Engineering. IEEE Computer Society Press, Los Alamitos, California.
  4. Dalal, S. R., Lyu, M. R., and Mallows, C. L. 2014. Software Reliability. John Wiley & Sons.
  5. Khan, R. A., Mustafa, K., and Ahson, S. I. 2004. Operation Profile-a key Factor for Reliability Estimation. University Press, Gautam Das and V. P. Gulati (Eds), CIT, 347-354.
  6. Ogheneovo, E. E. 2014. Software Dysfunction: Why Do Software Fail?. Journal of Computer and Communications, 2, 25-35.
  7. Rizvi, S. W. A., Singh, V. K., and Khan, R. A. 2016. Revisiting Software Reliability Engineering with Fuzzy Techniques. In:(IndiaCom–2016) Proc. of the 3rd IEEE Int. Conf. on Computing for Sustainable Global Development. Published by IEEExplore, 16-18 March, 2016. New Delhi, India.
  8. Yadav, H. B., and Yadav, D. K. 2014. Early Software Reliability Analysis using Reliability Relevant Software Metrics. International Journal of System Assurance Engineering and Management, pp.1-12.
  9. Rizvi, S. W. A., and Khan, R. A. 2010. Maintainability Estimation Model for Object-Oriented Software in Design Phase (MEMOOD). Journal of Computing, 2(4), 26-32.
  10. Rizvi, S. W. A., and Khan, R. A. 2009. A Critical Review on Software Maintainability Models. Proceedings of the Conference on Cutting Edge Computer and Electronics Technologies, 144-148.
  11. Pham, H. 2006. System Software Reliability. London: Reliability Engineering Series, Springer.
  12. Pandey, A. K., and Goyal, N. K. 2013. Early Software Reliability Prediction. Springer, India.
  13. Goel, A. L. 1985. Software Reliability Models: Assumptions, Limitations, and Applicability. IEEE Transaction on Software Engineering, 11(12), 1411-1423.
  14. Yadav, H. B., and Yadav, D. K. 2014. Early Software Reliability Analysis using Reliability Relevant Software Metrics. International Journal of System Assurance Engineering and Management,1-12.
  15. Zadeh, L. 1965. Fuzzy Sets. Information and Control, 8, 338-353.
  16. Khalsa, S. K. 2009. A Fuzzified Approach for the Prediction of Fault Proneness and Defect Density. In: Proceeding of World Congress on Eng., 1, 218-223.
  17. Yadav, O. P, Singh, N., Chinnam, R. B., and Goel, P. S. 2003. A Fuzzy Logic based approach to Reliability Improvement during Product Development. Reliability Engineering and System Safety, 80, 63-74.
  18. Yuan, D., and Zhang, C. 2011. Evaluation Strategy for Software Reliability Based on ANFIS. In: (ICECC-11) Proceedings of the IEEE International Conference on Electronics and Communications and Control, 3738-3741.
  19. Yadav, D. K., Charurvedi, S. K., and Mishra, R. B. 2012. Early Software Defects Prediction using Fuzzy Logic. International Journal of Performability Engineering, 8(4), 399-408.
  20. Aljahdali, S. 2011. Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic. Journal of Computer Science, 7(10),1574-1580.
  21. Cortellesa, V., Singh, H., and Cukic, B. 2002. Early Reliability Assessment of UML Based Software Models. In:Proceedings of the 3rd International Workshop on Software and Performance, 302–309.
  22. Wholin, C., and Runeson, P. 1998. Defect Content Estimations from Review Data. In:Proceedings of 20th International Conference on Software Engineering, 400-409.
  23. Yadav, H. B., and Yadav, D. K. 2015. A Fuzzy Logic based Approach for Phase-wise Software Defects Prediction using Software Metrics. Information and Software Technology, 63, 44-57.
  24. Rizvi, S. W. A., Singh, V. K., and Khan, R. A. 2016. The State of the Art in Software Reliability Prediction: Software Metrics and Fuzzy Logic Perspective. Advances in Intelligent Systems and Computing, Springer, 433, 629-637.
  25. Mohanta, S., Vinod, G., and Mall, R. A. 2011. Technique for Early Prediction of Software Reliability based on Design Metrics. International Journal of System Assurance Engineering and Management, 2(4), 261-281.
  26. He, P., Li, B., Liu, X., Chen, J., and Ma, Y. 2015. An Empirical Study on Software Defect Prediction with a Simplified Metric Set. Information and Software Technology, 59, 170-190.
  27. Li, M., and Smidts, C. 2003. A ranking of software engineering measures based on expert opinion. IEEE Transaction on Software Engineering, 29(9), 811–824.
  28. Martin, N., Fenton, N., and Nielson, L. 2000. Building large-scale Bayesian networks. The Knowledge Engineering review, 15(3), 257–284.
  29. Radjenovic, D., Hericko, M., Torkar, R., and Zivkovic, A. 2013. Software Fault Prediction Metrics: A Systematic Literature Review. Information and Software Technology, 55(8), 1397-1418.
  30. Andersson, M., and Vestergren, P. 2004. Object Oriented Design Quality Metrics. Uppsala Master’s Thesis in Computer Science 276, ISSN 11001836, 1-27.
  31. Bansiya, J., and Devis, C. 1997. Automated Metrics for Object-Oriented Development. Dr. Dobb’s Journal, 272(12), 42-48.
  32. Bansiya, J., and Devis, C. 2002. A Hierarchical Model for Object-Oriented Design Quality Assessment. IEEE Transactions on Software Engineering, 28(1), 4-17.
  33. Birkmeier, D. Q. 2010. On the State of the Art of Coupling and Cohesion Measures for Service-Oriented System Design metrics. Proceedings of Conference on Information Systems (AMCIS), 1-10.
  34. Breesam, K. M. 2007. Metrics for Object-Oriented Design Focusing on Class Inheritance Metrics. 2nd International Conference on Dependability of Computer Systems, June 14-16, 2007, IEEE Computer Society, 231-237.
  35. Dallal, J. A. 2010. Mathematical Validation of Object-Oriented Class Cohesion Metrics. International Journal of Computers, 4(2), 45-52.
  36. Gray, C. L. 2008. A Coupling Complexity Metric Suit for Predicting Software Quality. Thesis submitted to Polytechnic State University, California, 1-71.
  37. Yadav, A., and Khan, R. A. 2012. Development of Encapsulated Class Complexity Metric. International Conference on Computer, Communication, Control and Information Technology (CCCIT-2012), Procedia Technology, 754-760.
  38. Yadav, A., and Khan, R. A. 2011. Class Cohesion Complexity Metric (C3M). Proceedings of International Conference on Computer and Communication Technology (ICCCT-2011), 363-366.
  39. Yong, C., and Qingxin, Z. 2008. Improved Metrics for Encapsulation Based on Information Hiding. 9th International Conference for Young Computer Scientists, IEEE computer society, 742-724.
  40. Kumar, K. S., and Misra, R. B. 2008. An enhanced model for early software reliability prediction using software engineering metrics. Proceedings of 2nd International Conference on Secure System Integration and Reliability Improvement, 177–178.
  41. Ross, T. J. 2010. Fuzzy Logic with Engineering Applications. 3rd Edition, John Wiley and sons.
  42. Walkerden, F., and Jeffery, R. 1999. Analogy, Regression and Other Methods for Estimating Effort and Software Quality Attributes. Proceeding of European Conference Optimizing Software Development and Maintenance, 37-46.
  43. Conte, S. D., Dunsmore, H. F., and Shen, V. Y. 1986. Software Engineering Metrics and Models. ISBN: 0805321624, Benjamin Cummings Publishing Co., Inc., Redwood city, CA, USA.
Index Terms

Computer Science
Information Sciences

Keywords

Software Reliability Early Stage Prediction Fuzzy Logic Software Defects Software Metrics Software Reliability Model.