Description

A software bug is an error, flaw, failure, or fault in a computer program or system that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. Most bugs arise from mistakes and errors made by people in either a program’s source code or its design, or in frameworks and operating systems used by such programs, and a few are caused by compilers producing incorrect code. Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this project the system proposes some effective Software Data Reduction Techniques for Bug Triage. The system combine 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, the system extract attributes from historical bug data sets and build a predictive model for a new bug data set. This work provides an approach to form reduced and high quality bug data in software development and maintenance.