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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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%.

Original languageEnglish
Title of host publication2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185-186
Number of pages2
ISBN (Electronic)9781665436762
DOIs
Publication statusPublished - 2021
Event10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
Duration: 2021 Oct 122021 Oct 15

Publication series

Name2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/TerritoryJapan
CityKyoto
Period21/10/1221/10/15

Keywords

  • Adversarial Learning
  • Bug Fixing Time Prediction
  • Bug Report Analysis
  • Multi-task Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

Fingerprint

Dive into the research topics of 'Adversarial Multi-task Learning-based Bug Fixing Time and Severity Prediction'. Together they form a unique fingerprint.

Cite this