Volume & Issue no: Volume 6, Issue 5, September - October 2017
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Title: |
Empirical Analysis of Open Source projects using Feature Selection and Filtering Techniques |
Author Name: |
Prabujeet Kaur, Dharmendra Lal Gupta |
Abstract: |
Abstract: To achieve software quality for the large
systems results very costly. Developers and testers put a
lot of effort to investigate a large number of modules in
order to ensure the software quality. This tends to be very
time consuming process. The machine learning
techniques are used for fault prediction of the modules.
However, there are many modules which are very low in
priority for quality investigation. In this research,
Wrapper subset evaluation method has been used to
identify that subset of attributes which are most
prominent. 10, 20 and 30% of less complex faulty
software modules are filtered out from each attribute.
The remaining modules are used to build models against
four classifiers: Naïve Bayes, Support Vector Machine, k
nearest neighbors and C4.5 decision trees. The results of
the classifiers were analyzed and compared against the
filtering of less complex instances from LOC and NPM
metrics. The classifiers based on wrapper subset
evaluation method gave better results than the filtering of
LOC and NPM metrics.
Keywords: Software fault, complexity, CBO, NOA,
NMI, DIT, NOC, NAI, NPRIM, NPM, FAN-IN, FANOUT |
Cite this article: |
Prabujeet Kaur, Dharmendra Lal Gupta , "
Empirical Analysis of Open Source projects using Feature Selection and Filtering Techniques " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 6, Issue 5, September - October 2017 , pp.
075-082 , ISSN 2278-6856.
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