NDLI: Multiple kernel ensemble learning for software defect prediction

Content Provider	Springer Nature Link
Author	Wang, Tiejian Zhang, Zhiwu Jing, Xiaoyuan Zhang, Liqiang
Copyright Year	2015
Abstract	Software defect prediction aims to predict the defect proneness of new software modules with the historical defect data so as to improve the quality of a software system. Software historical defect data has a complicated structure and a marked characteristic of class-imbalance; how to fully analyze and utilize the existing historical defect data and build more precise and effective classifiers has attracted considerable researchers’ interest from both academia and industry. Multiple kernel learning and ensemble learning are effective techniques in the field of machine learning. Multiple kernel learning can map the historical defect data to a higher-dimensional feature space and make them express better, and ensemble learning can use a series of weak classifiers to reduce the bias generated by the majority class and obtain better predictive performance. In this paper, we propose to use the multiple kernel learning to predict software defect. By using the characteristics of the metrics mined from the open source software, we get a multiple kernel classifier through ensemble learning method, which has the advantages of both multiple kernel learning and ensemble learning. We thus propose a multiple kernel ensemble learning (MKEL) approach for software defect classification and prediction. Considering the cost of risk in software defect prediction, we design a new sample weight vector updating strategy to reduce the cost of risk caused by misclassifying defective modules as non-defective ones. We employ the widely used NASA MDP datasets as test data to evaluate the performance of all compared methods; experimental results show that MKEL outperforms several representative state-of-the-art defect prediction methods.
Starting Page	569
Ending Page	590
Page Count	22
File Format	PDF
ISSN	09288910
Journal	Automated Software Engineering
Volume Number	23
Issue Number	4
e-ISSN	15737535
Language	English
Publisher	Springer US
Publisher Date	2015-04-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Software defect prediction Multiple kernel learning Ensemble learning Multiple kernel ensemble learning (MKEL) Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems
Content Type	Text
Resource Type	Article
Subject	Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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