Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2020
Abstract
Cross-project defect prediction (CPDP), aiming to apply defect prediction models built on source projects to a target project, has been an active research topic. A variety of supervised CPDP methods and some simple unsupervised CPDP methods have been proposed. In a recent study, Zhou et al. found that simple unsupervised CPDP methods (i.e., ManualDown and ManualUp) have a prediction performance comparable or even superior to complex supervised CPDP methods. Therefore, they suggested that the ManualDown should be treated as the baseline when considering non-effort-aware performance measures (NPMs) and the ManualUp should be treated as the baseline when considering effort-aware performance measures (EPMs) in future CPDP studies. However, in that work, these unsupervised methods are only compared with existing supervised CPDP methods in terms of one or two NPMs and the prediction results of baselines are directly collected from the primary literature. Besides, the comparison has not considered other recently proposed EPMs, which consider context switches and developer fatigue due to initial false alarms. These limitations may not give a holistic comparison between the supervised methods and unsupervised methods. In this paper, we aim to revisit Zhou et al.'s study. To the best of our knowledge, we are the first to make a comparison between the existing supervised CPDP methods and the unsupervised methods proposed by Zhou et al. in the same experimental setting, considering both NPMs and EPMs. We also propose an improved supervised CPDP method EASC and make a further comparison between this method and the unsupervised methods. According to the results on 82 projects in terms of 12 performance measures, we find that when considering NPMs, EASC can achieve similar results with the unsupervised method ManualDown without statistically significant difference in most cases. However, when considering EPMs, our proposed supervised method EASC can statistically significantly outperform the unsupervised method ManualUp with a large improvement in terms of Cliff's delta in most cases. Therefore, the supervised CPDP methods are more promising than the unsupervised method in practical application scenarios, since the limitation of testing resource and the impact on developers cannot be ignored in these scenarios.
Keywords
Defect prediction, supervised model, unsupervised model, cross-project
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Transactions on Software Engineering
First Page
1
Last Page
16
ISSN
0098-5589
Identifier
10.1109/TSE.2020.3001739
Publisher
IEEE
Embargo Period
5-11-2021
Citation
NI, Chao; XIA, Xin; LO, David; CHEN, Xiang; and GU, Qing.
Revisiting supervised and unsupervised methods for effort-aware cross-project defect prediction. (2020). IEEE Transactions on Software Engineering. 1-16.
Available at: https://ink.library.smu.edu.sg/sis_research/5927
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/TSE.2020.3001739