a comparative study of absent features and unobserved values in software effort data

ISCAS OpenIR

	a comparative study of absent features and unobserved values in software effort data
	Zhang Wen; Yang Ye; Wang Qing
	2012
发表期刊	International Journal of Software Engineering and Knowledge Engineering
ISSN	0218-1940
卷号	22 期号:2 页码:185-202
摘要	Software effort data contains a large amount of missing values of project attributes. The problem of absent features, which occurred recently in machine learning, is often neglected by researchers of software engineering when handling the missingness in software effort data. In essence, absent features (structural missingness) and unobserved values (unstructured missingness) are different cases of missingness although their appearance in the data set are the same. This paper attempts to clarify the root cause of missingness of software effort data. When regarding missingness as absent features, we develop Max-margin regression to predict real effort of software projects. When regarding missingness as unobserved values, we use existing imputation techniques to impute missing values. Then, Ε -SVR is used to predict real effort of software projects with the input data sets. Experiments on ISBSG (International Software Benchmarking Standard Group) and CSBSG (Chinese Software Benchmarking Standard Group) data sets demonstrate that, with the tasks of effort prediction, the treatment regarding missingness in software effort data set as unobserved values can produce more desirable performance than that of regarding missingness as absent features. This paper is the first to introduce the concept of absent features to deal with missingness of software effort data. © 2012 World Scientific Publishing Company.; Software effort data contains a large amount of missing values of project attributes. The problem of absent features, which occurred recently in machine learning, is often neglected by researchers of software engineering when handling the missingness in software effort data. In essence, absent features (structural missingness) and unobserved values (unstructured missingness) are different cases of missingness although their appearance in the data set are the same. This paper attempts to clarify the root cause of missingness of software effort data. When regarding missingness as absent features, we develop Max-margin regression to predict real effort of software projects. When regarding missingness as unobserved values, we use existing imputation techniques to impute missing values. Then, Ε -SVR is used to predict real effort of software projects with the input data sets. Experiments on ISBSG (International Software Benchmarking Standard Group) and CSBSG (Chinese Software Benchmarking Standard Group) data sets demonstrate that, with the tasks of effort prediction, the treatment regarding missingness in software effort data set as unobserved values can produce more desirable performance than that of regarding missingness as absent features. This paper is the first to introduce the concept of absent features to deal with missingness of software effort data. © 2012 World Scientific Publishing Company.
收录类别	EI ; SCI
关键词	Forecasting Software Engineering
部门归属	(1) Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China
学科领域	Computer Science ; Engineering
资助者	National Natural Science Foundation of China 60903050, 71101138; Beijing Natural Science Fund 4122087; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
语种	英语
WOS记录号	WOS:000304829200003
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/14914
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhang Wen,Yang Ye,Wang Qing. a comparative study of absent features and unobserved values in software effort data[J]. International Journal of Software Engineering and Knowledge Engineering,2012,22(2):185-202.
APA	Zhang Wen,Yang Ye,&Wang Qing.(2012).a comparative study of absent features and unobserved values in software effort data.International Journal of Software Engineering and Knowledge Engineering,22(2),185-202.
MLA	Zhang Wen,et al."a comparative study of absent features and unobserved values in software effort data".International Journal of Software Engineering and Knowledge Engineering 22.2(2012):185-202.