JOURNAL ARTICLE

Improved Random Forest Algorithm for Software Defect Prediction through Data Mining Techniques

Kalai Magal.RShomona Gracia Jacob

Year: 2015 Journal:   International Journal of Computer Applications Vol: 117 (23)Pages: 18-22

Abstract

Software defect prediction using classification algorithms was advocated by many researchers.Moreover the classifier ensemble can effectively improve classification performance compared to a single classifier. The research on defect prediction using classifier ensemble methods are motivated since they have not been fully exploited.Software defects leads to failure of many defense systems. A comparative study of various classification methods was performed to classify software defects. The methods include Random Tree, Random

Keywords:
Computer science Random forest Data mining Software Data science Machine learning Programming language

Metrics

57
Cited By
10.27
FWCI (Field Weighted Citation Impact)
13
Refs
0.98
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Software Engineering Research
Physical Sciences →  Computer Science →  Information Systems
Software System Performance and Reliability
Physical Sciences →  Computer Science →  Computer Networks and Communications
Software Reliability and Analysis Research
Physical Sciences →  Computer Science →  Software
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