Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2018
Abstract
To help developers better allocate testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on past history of buggy classes. These techniques work well as long as a sufficient amount of data is available to train a prediction model. However, there is rarely enough training data for new software projects. To deal with this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, has been proposed and is regarded as a new challenge for defect prediction. So far, only a few cross-project defect prediction techniques have been proposed. To advance the state-of-the-art, in this work, we investigate 7 composite algorithms, which integrate multiple machine learning classifiers, to improve cross-project defect prediction. To evaluate the performance of the composite algorithms, we perform experiments on 10 open source software systems from the PROMISE repository which contain a total of 5,305 instances labeled as defective or clean. We compare the composite algorithms with CODEPLogistic, which is the latest cross-project defect prediction algorithm proposed by Panichella et al. [1], in terms of two standard evaluation metrics: cost effectiveness and F-measure. Our experiment results show that several algorithms outperform CODEPLogistic: Max performs the best in terms of F-measure and its average F-measure outperforms that of CODEPLogistic by 36.88%. BaggingJ48 performs the best in terms of cost effectiveness and its average cost effectiveness outperforms that of CODEPLogistic by 15.34%.
Keywords
Defect Prediction, Cross-Project, Classifier Combination
Discipline
Programming Languages and Compilers | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Frontiers of Computer Science
Volume
12
Issue
2
First Page
280
Last Page
296
ISSN
2095-2228
Identifier
10.1007/s11704-017-6015-y
Publisher
Springer
Citation
ZHANG, Yun; LO, David; XIA, Xin; and SUN, Jianling.
Combined classifier for cross-project defect prediction: An extended empirical study. (2018). Frontiers of Computer Science. 12, (2), 280-296.
Available at: https://ink.library.smu.edu.sg/sis_research/4130
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/10.1007/s11704-017-6015-y