An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis |
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Authors: | Yasunari?Takagi Email author" target="_blank">Osamu?MizunoEmail author Tohru?Kikuno |
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Affiliation: | (1) Social System Solutions Business Company, OMRON Corporation, Japan;(2) Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita Osaka, 565-0871, Japan |
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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. |
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Keywords: | Software risk management questionnaire risky project logistic regression |
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