Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-analysis

doi:10.1142/S0218194023500110

	Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-analysis
	Mei, Yuanqing 1; Rong, Yi 1; Liu, Shiran 1; Guo, Zhaoqiang 1; Yang, Yibiao 1; Lu, Hongmin 1; Tang, Yutian2 ; Zhou, Yuming 1
	2023-04-01
发表期刊	INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING (IF:0.6[JCR-2023],0.8[5-Year])
ISSN	0218-1940
EISSN	1793-6403
卷号	33 期号:5 页码:651-695
发表状态	已发表
DOI	10.1142/S0218194023500110
摘要	Many studies have explored the methods of deriving thresholds of object-oriented (i.e. OO) metrics. Unsupervised methods are mainly based on the distributions of metric values, while supervised methods principally rest on the relationships between metric values and defect-proneness of classes. The objective of this study is to empirically examine whether there are effective threshold values of OO metrics by analyzing existing threshold derivation methods with a large-scale meta-analysis. Based on five representative threshold derivation methods (i.e. VARL, ROC, BPP, MFM, and MGM) and 3268 releases from 65 Java projects, we first employ statistical meta-analysis and sensitivity analysis techniques to derive thresholds for 62 OO metrics on the training data. Then, we investigate the predictive performance of five candidate thresholds for each metric on the validation data to explore which of these candidate thresholds can be served as the threshold. Finally, we evaluate their predictive performance on the test data. The experimental results show that 26 of 62 metrics have the threshold effect and the derived thresholds by meta-analysis achieve promising results of GM values and significantly outperform almost all five representative (baseline) thresholds.
关键词	Object-oriented metric defect-proneness threshold meta-analysis
URL	查看原文
收录类别	SCI ; EI
语种	英语
资助项目	National Natural Science Foundation of China["62172205","62072194","62202306"]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic
WOS记录号	WOS:000975887200001
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
EI入藏号	20231814039463
EI主题词	Sensitivity analysis
EI分类号	921 Mathematics ; 951 Materials Science
原始文献类型	Journal article (JA)
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/301105
专题	信息科学与技术学院信息科学与技术学院_PI研究组_唐宇田组
通讯作者	Yang, Yibiao; Zhou, Yuming
作者单位	1.State Key Lab Novel Software Technol Nanjing Univ, P, R China, Nanjing, Peoples R China 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Mei, Yuanqing,Rong, Yi,Liu, Shiran,et al. Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-analysis[J]. INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING,2023,33(5):651-695.
APA	Mei, Yuanqing.,Rong, Yi.,Liu, Shiran.,Guo, Zhaoqiang.,Yang, Yibiao.,...&Zhou, Yuming.(2023).Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-analysis.INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING,33(5),651-695.
MLA	Mei, Yuanqing,et al."Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-analysis".INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 33.5(2023):651-695.