Predicting Bugs' Components via Mining Bug Reports

TL;DR

This paper presents a machine learning approach using support vector machines and Naive Bayes to automatically predict bug components from reports, significantly reducing triage time and improving bug fixing efficiency.

Contribution

It introduces a novel predictive model trained on historical bug reports to accurately classify bug components, enhancing bug triage automation.

Findings

Prediction accuracy up to 81.21% on Eclipse data

Average bug fixing delay reduced by 54.3 days

Effective use of text classification for bug component prediction

Abstract

The number of bug reports in complex software increases dramatically. Now bugs are triaged manually, bug triage or assignment is a labor-intensive and time-consuming task. Without knowledge about the structure of the software, testers often specify the component of a new bug wrongly. Meanwhile, it is difficult for triagers to determine the component of the bug only by its description. We dig out the components of 28,829 bugs in Eclipse bug project have been specified wrongly and modified at least once. It results in these bugs have to be reassigned and delays the process of bug fixing. The average time of fixing wrongly-specified bugs is longer than that of correctly-specified ones. In order to solve the problem automatically, we use historical fixed bug reports as training corpus and build classifiers based on support vector machines and Na\"ive Bayes to predict the component of a new bug. The best prediction accuracy reaches up to 81.21% on our validation corpus of Eclipse project. Averagely our predictive model can save about 54.3 days for triagers and developers to repair a bug. Keywords: bug reports; bug triage; text classification; predictive model