Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : Mariya Davis | C Nandhakumarunnamed

Volume 03 Issue 12 2016

ISSN no:  2348-3121

Page no: 101-104

Abstract – Software bugs are unavoidable and it is an expensive task for fixing software bugs and over 45 percentage of cost in dealing with software bugs by the software companies. The inevitable step of bug triage is carried out to fix a new bug automatically by applying text classification techniques which reduce the cost and time of manual work. In this paper, the problem of data reduction for bug triage is addressed. The proposed technique combines instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, it extracts attributes from historical bug data sets and builds a predictive model for a new bug data set. The results show that the data reduction can effectively reduce the data scale and improve the accuracy of bug triage. The proposed system provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance. It also focuses on the quality of bug data and can be utilized as a preprocessing technique for bug triage, which improves both data quality and reduces data scale. 

Keywords— Mining software repositories, Bug reduction, Feature selection, Instance selection, Bug triage


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