A Novel Approach on Towards Effective Bug Triage and Improve the Quality of Bug Data

G Praveen, V. Sridhar Reddy, Shaik Abdul Nabi

Abstract


In order to diminution the time cost in physical work, text classification practices are pragmatic to conduct automatic bug triage. Presently, software companies spend over 45 percent of cost in dealing with software bugs. A foreseeable step of fixing bugs is bug triage, which ambitions to decorously consign a developer to a new bug. In this scheme, it is addressed the problem of data reduction for bug triage, to reduce the scale and progress the reputation of bug data. It is here combined with instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To define the order of applying instance selection and feature selection, it is extract attributes from historical bug data sets and build a predictive model for a new bug data set. It is practically scrutinized the enactment of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.

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DOI: https://doi.org/10.23956/ijarcsse.v7i9.402

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