Sentence embedding and fine-tuning to automatically identify duplicate bugs - Isotani, Haruna; Washizaki, Hironori; Fukazawa, Yoshiaki; Nomoto, Tsutomu; Ouji, Saori; Saito, Shinobu (Frontiers in Computer Science, 2023)
Industrial software maintenance is critical but burdensome. Activities such as detecting duplicate bug reports are often performed manually. Herein an automated duplicate bug report detection system improves maintenance efficiency using vectorization of the contents and deep learning–based sentence embedding to calculate the similarity of the whole report from vectors of individual elements. Specifically, sentence embedding is realized using Sentence-BERT fine tuning. Additionally, to validate the proposed system, its performance is experimentally compared to baseline methods. The proposed system detects duplicate bug reports more effectively than existing methods.