Finding Successful and Failure of Software using Intelligence Techniques

Authors

  • Janki Sharan Pahareeya  Department of Information Technology, Rustamji Institute of Technology, Tekanpur, Madhya Pradesh, India
  • Sanjay Patsariya  Department of Information Technology, Rustamji Institute of Technology, Tekanpur, Madhya Pradesh, India
  • Anand Jha  Department of Information Technology, Rustamji Institute of Technology, Tekanpur, Madhya Pradesh, India
  • Aradhana Saxena  Department of Information Technology, Rustamji Institute of Technology, Tekanpur, Madhya Pradesh, India

Keywords:

Vector machine (SVM), Classification and Regression Tree (CART), Multilayer (MLP), J-48, Random forest, software reuse.

Abstract

This paper presents computational intelligence techniques for software reuse prediction. In this paper, we did comparative study of five computational intelligence techniques that are J-48, Naive-Bayes Classification Algorithm, MLP, random forest and SVM on software reuse data set. We also performed CART based feature selection for reducing the attributes of the data. Ten-fold cross validation is performed throughout the study. The results obtained from our experiments indicate that after feature selection all five techniques were performed well.

References

  1. Tribhuvan A.P., Tribhuvan P.P. and Gade J.G. (2015), “Applying Naive Bayesian Classifier for Predicting Performance of a Student Using WEKA. Advances in Computational Research”, ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 7, Issue 1, pp.-239-242.
  2. Dunham M.H. (2006) “Data mining: Introductory and advanced topics”, Pearson Education India.
  3. Jankisharan Pahariya, V. Ravi and M. Carr, “Software Cost Estimation Using Computational Intelligent Techniques”, International Conference on Computer Information System and Industrial Management Application, Coimbatore, ISBN: 978-1-4244-5053-4 ,pp.849-854Tamil Nadu, India,2009.
  4. S.Pahariya, V. Ravi, M. Carr and M.Vasu,“ Computational Intelligence Hybrids Applied to Software Cost Estimation”, International Journal of Computer Information Systems and Industrial Management Applications, ISSN: 2150-7988 Vol.2 (2010), pp.104-112.
  5. Andreou and E. Papatheocharous, “Software Cost Estimation using Fuzzy Decision Trees”, Proceedings of 23rd IEEE/ACM International Conference on Automated Software Engineering, 2008, pp. 371-374.
  6. Mittal, K. Prakash and H. Mittal, “Software Cost Estimation Using Fuzzy Logic”, ACM SIGSOFT Software Engineering Notes, 2010, 35(1), pp. 1-7.
  7. Attarzadch, “Improving the accuracy of software cost estimation model based on a new fuzzy logic model”, World applied sciences journal, 2010, 8(2), pp. 177-184.
  8. K. Aggarwal, Y. Singh, P. Chandra and M. Puri, “An expert committee model to estimate line of code”, ACM SIGSOFT Software Engineering Notes, 2005, pp. 1-4.
  9. Vinay Kumar, V. Ravi and M. Carr, “Software Cost Estimation using Soft Computing Approaches”,Handbook on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, Eds. E.Soria, J.D. Martin, R. Magdalena, M.Martinez, A.J.Serrano, IGI Global, USA, 2009.
  10. F. Li, M. Xie and T.N. Goh, “A study of project selection and feature weighting for analogy based software cost estimation”, Journal of Systems and Software, 2009, 82(2), pp. 241–252.
  11. R. Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers, 1993.
  12. Sankar K. Pal and Sushmita Mitra, "Multilayer Perceptron, Fuzzy sets and Classification", IEEE Transection on Neural Networks, Vol. 3No.5,SEptember(1992)
  13. Rosenblatt, “Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms”, Spartan Books, Washington DC, 1961.
  14. Cybenko, “Approximation by superpositions of a sigmoidal function”, Mathematics of Control, Signals, and Systems, Vol.2(1989), PP. 303-314.
  15. E. Rumelhart and J.L. McClelland,Eds.," Parallel Distributed Processing", Vol. 1, Cambridge, MA: MIT press,1986.
  16. E. Fahlman and G.E. Hinton," Connectionist architecture for artificial intelligence", IEEE Computer, PP. 100-109, 1987
  17. N. Vapnik, “Statistical Learning Theory”, John Wiley,New York, 1998.
  18. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines John C. Platt Microsoft Research.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Janki Sharan Pahareeya, Sanjay Patsariya, Anand Jha, Aradhana Saxena, " Finding Successful and Failure of Software using Intelligence Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1064-1068, January-February-2018.