Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2015
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.
Keywords
Cost Effectiveness, Deep Belief Network, Deep Learning, Just-In-Time Defect Prediction
Discipline
Numerical Analysis and Scientific Computing | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2015 IEEE International Conference on Software Quality, Reliability and Security, QRS 2015: 3-5 August, Vancouver, Canada: Proceedings
First Page
17
Last Page
26
ISBN
9781467379892
Identifier
10.1109/QRS.2015.14
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
YANG, Xinli; David LO; XIA, Xin; ZHANG, Yun; and SUN, Jianling.
Deep Learning for Just-In-Time Defect Prediction. (2015). 2015 IEEE International Conference on Software Quality, Reliability and Security, QRS 2015: 3-5 August, Vancouver, Canada: Proceedings. 17-26.
Available at: https://ink.library.smu.edu.sg/sis_research/3096
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/QRS.2015.14