Study on Automatic Defect Report Classification System with Self Attention Visualization

In recent years, software in devices such as smartphones and tablets has become increasingly multifunctional, and the use of OSS has become essential. In software development using large-scale OSS, it is important to report defects to appropriate personnel promptly. In this paper, we propose a method to classifying defect reports into appropriate categories using fine-tuned BERT and visualize self-attention information. In the evaluation, category classification was performed using defect reports of the actual OSS project. The F1 score was 0.87, which indicated that high-accuracy classification was possible. Also, the visualization results show that category-specific words can be extracted.