An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis
An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis
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DOI：
10.1007/s10664-005-3864-z
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发表时间：
2005-10
影响因子：
4.1
通讯作者：
Yasunari Takagi;O. Mizuno;T. Kikuno
中科院分区：
文献类型：
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作者：
Yasunari Takagi;O. Mizuno;T. Kikuno
在软件开发过程中，项目经常会遇到风险情况。如果项目未能检测到此类风险，它们可能会表现出混乱的行为。在本文中，我们提出了一种基于实证调查问卷来表征项目所表现出的混乱程度的新方案。首先，我们从需求、估算、计划、团队组织和项目管理活动五个项目角度设计了一份调查问卷。这些观点中的每一个都通过确定软件风险的经验和知识的问题进行评估。其次，我们使用生成的指标数据将项目分为“困惑”和“不困惑”。第三，我们使用逻辑回归分析分析了问卷答复与项目混乱程度之间的关系，并构建了一个模型来表征混乱项目。使用实际项目数据的实验结果表明，32个项目中的28个被正确表征。结果，我们得出的结论是，对混乱项目的描述是成功的。此外，我们将构建的模型应用于其他项目的数据，以检测有风险的项目。应用这一概念的结果表明，8 个项目中有 7 个被正确分类。因此，我们得出的结论是，所提出的方案也适用于风险项目的检测。
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.