DeepJIT: An end-to-end deep learning framework for just-in-time defect prediction

Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi

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

4 Citations (Scopus)

Abstract

Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction - aka. Just-In-Time (JIT) defect prediction - has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones. Most existing JIT defect prediction approaches make use of manually engineered features. Unlike those approaches, in this paper, we propose an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects. Experiments on two popular software projects (i.e., QT and OPENSTACK) on three evaluation settings (i.e., cross-validation, short-period, and long-period) show that the best variant of DeepJIT (DeepJIT-Combined), compared with the best performing state-of-the-art approach, achieves improvements of 10.36-11.02% for the project QT and 9.51-13.69% for the project OPENSTACK in terms of the Area Under the Curve (AUC).

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/ACM 16th International Conference on Mining Software Repositories, MSR 2019
PublisherIEEE Computer Society
Pages34-45
Number of pages12
ISBN (Electronic)9781728134123
DOIs
Publication statusPublished - May 2019
Event16th IEEE/ACM International Conference on Mining Software Repositories, MSR 2019 - Montreal, Canada
Duration: May 26 2019May 27 2019

Publication series

NameIEEE International Working Conference on Mining Software Repositories
Volume2019-May
ISSN (Print)2160-1852
ISSN (Electronic)2160-1860

Conference

Conference16th IEEE/ACM International Conference on Mining Software Repositories, MSR 2019
CountryCanada
CityMontreal
Period5/26/195/27/19

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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    Hoang, T., Khanh Dam, H., Kamei, Y., Lo, D., & Ubayashi, N. (2019). DeepJIT: An end-to-end deep learning framework for just-in-time defect prediction. In Proceedings - 2019 IEEE/ACM 16th International Conference on Mining Software Repositories, MSR 2019 (pp. 34-45). [8816772] (IEEE International Working Conference on Mining Software Repositories; Vol. 2019-May). IEEE Computer Society. https://doi.org/10.1109/MSR.2019.00016