Deep learning for just-in-time defect prediction

Xinli Yang, David Lo, Xin Xia, Yun Zhang, Jianling Sun

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

96 Citations (Scopus)

Abstract

Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of expressive features from a set of initial change features by leveraging a deep belief network algorithm. Next, a machine learning classifier is built on the selected features. To evaluate the performance of our approach, we use datasets from six large open source projects, i.e., Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. We compare our approach with the approach proposed by Kamei et al. The experimental results show that on average across the 6 projects, Deeper could discover 32.22% more bugs than Kamei et al's approach (51.04% versus 18.82% on average). In addition, Deeper can achieve F1-scores of 0.22-0.63, which are statistically significantly higher than those of Kamei et al.'s approach on 4 out of the 6 projects.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Software Quality, Reliability and Security, QRS 2015
Subtitle of host publication3–5 August 2015 Vancouver, British Columbia, Canada
EditorsJian Zhang, Bhavani Thuraisingham
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages17-26
Number of pages10
ISBN (Electronic)9781467379892, 9781467379885
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Conference on Software Quality, Reliability and Security 2015 - Vancouver, Canada
Duration: 3 Aug 20155 Aug 2015
https://paris.utdallas.edu/qrs15/

Conference

ConferenceIEEE International Conference on Software Quality, Reliability and Security 2015
Abbreviated titleQRS 2015
CountryCanada
CityVancouver
Period3/08/155/08/15
Internet address

Keywords

  • Cost Effectiveness
  • Deep Belief Network
  • Deep Learning
  • Just-In-Time Defect Prediction

Cite this

Yang, X., Lo, D., Xia, X., Zhang, Y., & Sun, J. (2015). Deep learning for just-in-time defect prediction. In J. Zhang, & B. Thuraisingham (Eds.), Proceedings - 2015 IEEE International Conference on Software Quality, Reliability and Security, QRS 2015: 3–5 August 2015 Vancouver, British Columbia, Canada (pp. 17-26). [7272910] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/QRS.2015.14