Dae-Sung Wang  Hoon Seong  Chan-Gun Lee

10.5626/JOK.2022.49.11.987

bug management bug fixer assignment Machine Learning Natural Language Processing pre trained language model

The scale and complexity of software continue to increase; hence they contribute to the occurrence of diverse bugs. Therefore, the necessity of systematic bug management has been raised. A few studies have proposed automating the assignment of bug fixers using word-based deep learning models. However, their accuracy is not satisfactory due to context of the word is ignored, and there is an excessive number of classes. In this paper, the accuracy was improved by about 27%p over the top-10 accuracies by using a fine-tuned pre-trained language model based on BERT, RoBERTa, DeBERTa, and CodeBERT. Experiments confirmed that the accuracy was about 70%. Through this, we showed that the fine-tuned pretrained language model could be effectively applied to automated bug-fixer assignments.