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 expectationmaximization 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.
To solve the problem of low quality of bug reports, Xuan et al. REF propose a semi-supervised learning approach with a weighted recommendation list for bug triage.
Automatic Bug Triage using Semi-Supervised Text Classification
ArXiv
ArXiv
Computer Science