Duplicate Bug Report Detection by Using Sentence Embedding and Fine-tuning
Haruna Isotani, Hironori Washizaki, Yoshiaki Fukazawa, Tsutomu Nomoto, Saori Ouji, Shinobu SAITO
Abstract
Industrial software maintenance devotes much time and effort to find duplicate bug reports. In this paper, we propose an automated duplicate bug report detection system to improve software maintenance efficiency. Our system detects duplicate reports by vectorizing the contents of each report item by deep-learning-based sentence embedding and calculating the similarity of the whole report from those of the item vectors. The Sentence-BERT fine-tuned with report texts is used for sentence embedding. Finally, we verify that the combination of processing separately by item and Sentence-BERT fine-tuned with reports effectively detects duplicate bug reports in industrial experiments that compare the performance of existing methods.