Duplicate Bug Report Detection by Using Sentence Embedding and Fine-tuning

Haruna Isotani, Hironori Washizaki, Yoshiaki Fukazawa, Tsutomu Nomoto, Saori Ouji, Shinobu Saito

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE International Conference on Software Maintenance and Evolution, ICSME 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ535-544
ページ数10
ISBN(電子版)9781665428828
DOI
出版ステータスPublished - 2021
イベント37th IEEE International Conference on Software Maintenance and Evolution, ICSME 2021 - Luxembourg City, Luxembourg
継続期間: 2021 9月 272021 10月 1

出版物シリーズ

名前Proceedings - 2021 IEEE International Conference on Software Maintenance and Evolution, ICSME 2021

Conference

Conference37th IEEE International Conference on Software Maintenance and Evolution, ICSME 2021
国/地域Luxembourg
CityLuxembourg City
Period21/9/2721/10/1

ASJC Scopus subject areas

  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理

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