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Improving Bug Triage Based On Predictive Model


Author(s):

K.Soundarya , Christian College of Engineering and Technology Dindigul, Tamilnadu-624619, India; M.Soundhariya, Christian College of Engineering and Technology Dindigul, Tamilnadu-624619, India; V.Vergin Suganya, Christian College of Engineering and Technology Dindigul, Tamilnadu-624619, India; Mrs.S.Christina Magneta, Christian College of Engineering and Technology Dindigul, Tamilnadu-624619, India

Keywords:

Bug triage, CHI feature selection, ICF instance selection, data reduction, predictive model

Abstract:

Lot of MNC companies invest more time and cost to deals with bug report day by day. An necessary step of fixing the bugs is bug triage, which aims to properly assign the programmer for a new bug. To avoid the time increment in manual, text classification and binary classification techniques are used. Data reduction for bug triage is take in hand hence we merge the instance selection and feature selection which concurrently decrease the data size and recover the accuracy of bug reports in bug triage. To find out the order of applying the two algorithms namely CHI feature selection and ICF instance selection by using predictive model to produce high quality of bug data. Bug reports are converted into bar chart model. Finally, produce the output as graphical representation. The output shows that data reduction can successfully reduce the data size and increase the accuracy of bug triage.


Other Details:

Manuscript Id :IJSTEV2I10048
Published in :Volume : 2, Issue : 10
Publication Date: 01/05/2016
Page(s): 174-178
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