Finding Successful and Failure of Software using Intelligence Techniques

Authors(4) :-Janki Sharan Pahareeya, Sanjay Patsariya, Anand Jha, Aradhana Saxena

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1064-1068
Manuscript Number : CSEIT1831240
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

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.
Journal URL : http://ijsrcseit.com/CSEIT1831240

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