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Subject: Computer Science ; Engineering
Title:
a comparative study of absent features and unobserved values in software effort data
Author: Zhang Wen ; Yang Ye ; Wang Qing
Keyword: Forecasting ; Software engineering
Source: International Journal of Software Engineering and Knowledge Engineering
Issued Date: 2012
Volume: 22, Issue:2, Pages:185-202
Indexed Type: EI ; SCI
Department: (1) Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China
Sponsorship: 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
Abstract: 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.
English Abstract: 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.
Language: 英语
WOS ID: WOS:000304829200003
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Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/14914
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
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,2012-01-01,22(2):185-202.
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