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.

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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, IInternational 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.