USING DATA REDUCTION TECHNIQUES FOR EFFECTIVE BUG TRIAGE
Sri Vasavi College, Erode Self-Finance Wing, 3rd February 2017. National Conference on Computer and Communication, NCCC’17. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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Abstract
Data mining is the practice of evaluating large pre-existing set of data in order to generate the new proceeds data. Bug triaging is just like a part of mining in which it assign a bug from large data set to the developer. Currently, large amount of economic share of IT sector is spending on manually handling the bugs. Triaging is incredible aspect of development that benefits users and developer alike. It was seen that in numbers of approaches they try to reduce the bugs assigning problem but also suffer from number of problems like economic rise, redundant data, poor productivity etc. To overcome all issues Bud triage with reduction technique is adapted by us. It uses Feature Selection and Instance selection reduction algorithm which will help to reduce the data by scale and dimension and provide us with rich repository of data. This will be beneficial to predict out the correct developer to whom the bug can be assigned. Anatomization (Analysis) of Bug triage will show that how the combination of Feature selection and Instance selection will help to achieve the good set of result.
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