Adversarial Multi-task Learning-based Bug Fixing Time and Severity Prediction

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Bug reports contain important information for software quality assurance. Conventionally, engineers complete bug report analysis tasks, which are extremely burdensome. Recently, researchers and companies have been working towards the automation of bug report analysis. Many machine learning and deep learning models are utilized for triage and prediction of bug report attributes such as bug fixing time and bug severity based on the description and comment text in bug reports. However, due to the rapid growth of data size in bug reporting systems, the prediction accuracy in single-task machine learning models is neither efficient nor effective. Multi-task learning (MTL) is a transfer learning scheme, which can train multiple related tasks together, reducing the training time while improving the overall performance. In our study, we utilize adversarial multi-task learning, which addresses the problem of contaminated shared feature space in common MTL models towards a purer shared feature space. Our adversarial convolutional neural network model (ADV-CNN) improved the validation accuracy of the bug fixing time prediction from 83.67% of a ST-CNN model to 89.25%.

本文言語English
ホスト出版物のタイトル2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ185-186
ページ数2
ISBN(電子版)9781665436762
DOI
出版ステータスPublished - 2021
イベント10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
継続期間: 2021 10月 122021 10月 15

出版物シリーズ

名前2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
国/地域Japan
CityKyoto
Period21/10/1221/10/15

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 信号処理
  • 生体医工学
  • 電子工学および電気工学
  • メディア記述
  • 器械工学

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