Sentence embedding and fine-tuning to automatically identify duplicate bugs

被引：0

作者：

Isotani, Haruna ^{[1
]}

Washizaki, Hironori ^{[1
]}

Fukazawa, Yoshiaki ^{[1
]}

Nomoto, Tsutomu ^{[2
]}

Ouji, Saori ^{[3
]}

Saito, Shinobu ^{[3
]}

机构：

[1] Waseda Univ, Dept Comp Sci & Engn, Tokyo, Japan

[2] NTT CORP, Software Innovat Ctr, Tokyo, Japan

[3] NTT CORP, Comp & Data Sci Labs, Tokyo, Japan

来源：

FRONTIERS IN COMPUTER SCIENCE | 2023年 / 4卷

关键词：

bug reports; duplicate detection; BERT; sentence embedding; natural language processing; information retrieval;

D O I：

10.3389/fcomp.2022.1032452

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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, its performance is experimentally compared to baseline methods to validate the proposed system. The proposed system detects duplicate bug reports more effectively than existing methods.

引用

页数：13

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