Automatic Bug Triage using Semi-Supervised Text Classification

TL;DR

This paper introduces a semi-supervised text classification method for bug triage that leverages both labeled and unlabeled bug reports, improving accuracy over traditional supervised methods.

Contribution

It combines naive Bayes and expectation-maximization to effectively utilize unlabeled bug reports and incorporates developer weights to enhance bug triage performance.

Findings

Outperforms existing supervised methods in accuracy

Effectively uses unlabeled bug reports for training

Incorporates developer weights to improve classification

Abstract

In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectation-maximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy.