Towards Effective Bug Triage with Towards Effective Bug Triage with Software Data Reduction Techniques
This work addresses the high cost of manual bug triage in software development, offering a data reduction approach to enhance efficiency, though it is incremental as it builds on existing text classification techniques.
The paper tackles the problem of reducing the scale and improving the quality of bug data for automatic bug triage, combining instance and feature selection to achieve effective data reduction and accuracy improvement in bug triage on 600,000 bug reports from Eclipse and Mozilla.
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 paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We 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, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.