Knowledge Commons of Institute of Automation,CAS
Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM | |
Yu, Xiao1,2; Liu, Jin1,3,4; Keung, Jacky Wai2; Li, Qing5; Bennin, Kwabena Ebo6; Xu, Zhou1; Wang, Junping7![]() | |
发表期刊 | IEEE TRANSACTIONS ON RELIABILITY
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ISSN | 0018-9529 |
2020-03-01 | |
卷号 | 69期号:1页码:139-153 |
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. |
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) |
DOI | 10.1109/TR.2019.2931559 |
关键词[WOS] | SUPPORT VECTOR MACHINE ; GENETIC ALGORITHM ; FEATURE-SELECTION ; NEURAL-NETWORKS ; COUNT MODELS ; SOFTWARE ; CLASSIFICATION ; REGRESSION ; NUMBER ; FAULTS |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Hardware & Architecture ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000526289100010 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38857 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
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.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China 6.Blekinge Inst Technol, Dept Software Engn, S-37134 Karlskrona, Sweden 7.Chinese Acad Sci, Inst Automat, Lab Precis Sensing & Control Ctr, Beijing 100000, Peoples R China 8.Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, 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. |
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