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Y. Takagi, O. Mizuno, and T. Kikuno, "An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis," Empirical Software Engineering, 10(4), pp. 495-515, December 2005.
ID 424
分類 学術論文誌(査読付)
タグ analysis characterizing empirical logistic projects regression risky software major-mizuno
表題 (title) An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis
表題 (英文)
著者名 (author) Yasunari Takagi,Osamu Mizuno,Tohru Kikuno
英文著者名 (author)
キー (key) Yasunari Takagi, Osamu Mizuno, Tohru Kikuno
定期刊行物名 (journal) Empirical Software Engineering
定期刊行物名 (英文)
巻数 (volume) 10
号数 (number) 4
ページ範囲 (pages) 495-515
刊行月 (month) 12
出版年 (year) 2005
Impact Factor (JCR) 0.966 (2005)
URL http://www.springerlink.com/content/336u163784807572/
付加情報 (note)
注釈 (annote)
内容梗概 (abstract) During software development, projects often experience risky
situations. If projects fail to detect such risks, they may
exhibit confused behavior. In this paper, we propose a new scheme
for characterization of the level of confusion exhibited by projects
based on an empirical questionnaire. First, we designed a
questionnaire from five project viewpoints, requirements, estimates,
planning, team organization, and project management activities. Each
of these viewpoints was assessed using questions in which experience
and knowledge of software risks are determined. Secondly, we
classify projects into ``confused'' and ``not confused'', using the
resulting metrics data. We thirdly analyzed the relationship between
responses to the questionnaire and the degree of confusion of the
projects using logistic regression analysis and constructing a model
to characterize confused projects. The experimental result used
actual project data shows that 28 projects out of 32 were
characterized correctly. As a result, we concluded that the
characterization of confused projects was successful. Furthermore,
we applied the constructed model to data from other projects in
order to detect risky projects. The result of the application of
this concept showed that 7 out of 8 projects were classified
correctly. Therefore, we concluded that the proposed scheme is also
applicable to the detection of risky projects.


論文電子ファイル draft (application/pdf) [一般閲覧可]
BiBTeXエントリ 
@article{id424,
         title = {An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis},
        author = {Yasunari Takagi and Osamu Mizuno and Tohru Kikuno},
       journal = {Empirical Software Engineering},
        volume = {10},
        number = {4},
         pages = {495-515},
         month = {12},
          year = {2005},
    impactfactor = {0.966 (2005)},
}