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Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM

文献类型：期刊论文

作者Yu, Xiao1,2; Liu, Jin1,3,4; Keung, Jacky Wai2; Li, Qing6; Bennin, Kwabena Ebo7; Xu, Zhou1; Wang, Junping8; Cui, Xiaohui5
刊名IEEE TRANSACTIONS ON RELIABILITY
出版日期2020-03-01
卷号69期号:1页码:139-153
关键词Support vector machines Software Prediction algorithms Predictive models Testing Software algorithms Computer science Cost-sensitive learning data imbalance ranking-oriented defect prediction (RODP)
ISSN号0018-9529
DOI10.1109/TR.2019.2931559
通讯作者Liu, Jin(jinliu@whu.edu.cn) ; Keung, Jacky Wai(jacky.keung@cityu.edu.hk)
英文摘要Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook that ranking a module with more defects incorrectly makes it difficult to successfully find all of the defects in the module due to fewer testing resources being allocated to the module, which results in much higher costs than incorrectly ranking the modules with fewer defects, and the numbers of defects in software modules are highly imbalanced in defective software datasets. Cost-sensitive learning is an effective technique in handling the cost issue and data imbalance problem for software defect prediction. However, the effectiveness of cost-sensitive learning has not been investigated in RODP models. Aims: In this article, we propose a cost-sensitive ranking support vector machine (SVM) (CSRankSVM) algorithm to improve the performance of RODP models. Method: CSRankSVM modifies the loss function of the ranking SVM algorithm by adding two penalty parameters to address both the cost issue and the data imbalance problem. Additionally, the loss function of the CSRankSVM is optimized using a genetic algorithm. Results: The experimental results for 11 project datasets with 41 releases show that CSRankSVM achieves 1.12%-15.68% higher average fault percentile average (FPA) values than the five existing RODP methods (i.e., decision tree regression, linear regression, Bayesian ridge regression, ranking SVM, and learning-to-rank (LTR)) and 1.08%-15.74% higher average FPA values than the four data imbalance learning methods (i.e., random undersampling and a synthetic minority oversampling technique; two data resampling methods; RankBoost, an ensemble learning method; IRSVM, a CSRankSVM method for information retrieval). Conclusion: CSRankSVM is capable of handling the cost issue and data imbalance problem in RODP methods and achieves better performance. Therefore, CSRankSVM is recommended as an effective method for RODP.
WOS关键词SUPPORT VECTOR MACHINE ; GENETIC ALGORITHM ; FEATURE-SELECTION ; NEURAL-NETWORKS ; COUNT MODELS ; SOFTWARE ; CLASSIFICATION ; REGRESSION ; NUMBER ; FAULTS
资助项目National Key R&D Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61572374] ; National Natural Science Foundation of China[U163620068] ; National Natural Science Foundation of China[U1135005] ; National Natural Science Foundation of China[61572371] ; National Natural Science Foundation of China[61772525] ; Open Fund of Key Laboratory of Network Assessment Technology from CAS ; Guangxi Key Laboratory of Trusted Software[kx201607] ; Academic Team Building Plan for Young Scholars from Wuhan University[WHU2016012] ; General Research Fund of the Research Grants Council of Hong Kong[11208017] ; City University of Hong Kong[9678149] ; City University of Hong Kong[7005028] ; Intel[9220097] ; Hong Kong Polytechnic University[9B0V]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000526289100010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Open Fund of Key Laboratory of Network Assessment Technology from CAS ; Guangxi Key Laboratory of Trusted Software ; Academic Team Building Plan for Young Scholars from Wuhan University ; General Research Fund of the Research Grants Council of Hong Kong ; City University of Hong Kong ; Intel ; Hong Kong Polytechnic University
源URL[http://ir.ia.ac.cn/handle/173211/38856]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Liu, Jin; Keung, Jacky Wai
作者单位1.Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China
2.City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
3.Chinese Acad Sci, Inst Informat Engn, Key Lab Network Technol, Beijing 100000, Peoples R China
4.Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541000, Peoples R China
5.Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China
6.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
7.Blekinge Inst Technol, Dept Software Engn, S-37134 Karlskrona, Sweden
8.Chinese Acad Sci, Inst Automat, Lab Precis Sensing & Control Ctr, Beijing 100000, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Xiao,Liu, Jin,Keung, Jacky Wai,et al. Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM[J]. IEEE TRANSACTIONS ON RELIABILITY,2020,69(1):139-153.
APA Yu, Xiao.,Liu, Jin.,Keung, Jacky Wai.,Li, Qing.,Bennin, Kwabena Ebo.,...&Cui, Xiaohui.(2020).Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM.IEEE TRANSACTIONS ON RELIABILITY,69(1),139-153.
MLA Yu, Xiao,et al."Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM".IEEE TRANSACTIONS ON RELIABILITY 69.1(2020):139-153.

入库方式： OAI收割

来源：自动化研究所

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