Data envelopment analysis (DEA) models are often used for benchmarking software projects, and traditional DEA models only allow for the use of continuous variables. This study considers the use of DEA for datasets with mixed continuous and discrete variables to ranking software projects. It uses the existing radial DEA model and extends the nonradial DEA model to allow for the use of mixed variables. Further efficiency scores from the two DEA models are averaged in an ensemble DEA score. Using three real-world software engineering datasets, this study finds that the nonradial DEA and the ensemble DEA models have better discriminating power (lower tied efficiency scores) to rank software projects, and the radial DEA model generates more general ranking distribution (higher entropy) of normalized efficiency scores. The choice between selecting radial and nonradial DEA models for ranking software projects appears to depend on the extent to which managers want to introduce bias into the efficiency score ranking distribution. Radial models appear to have a lower bias than nonradial models. The ensemble DEA model appears to be the best performing DEA model for datasets containing two or more discrete and continuous variables.

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混合数据的定量软件项目管理：径向、非径向和集成数据包络分析模型的比较

数据包络分析 (DEA) 模型通常用于对软件项目进行基准测试，而传统的 DEA 模型仅允许使用连续变量。本研究考虑将 DEA 用于具有混合连续和离散变量的数据集以对软件项目进行排名。它使用现有的径向 DEA 模型并扩展非径向 DEA 模型以允许使用混合变量。来自两个 DEA 模型的进一步效率得分在整体 DEA 得分中取平均值。本研究使用三个真实世界的软件工程数据集，发现非径向 DEA 和集成 DEA 模型在对软件项目进行排名时具有更好的区分能力（较低的绑定效率分数），而径向 DEA 模型生成更一般的排名分布（更高的熵）归一化的效率得分。在为软件项目排名选择径向和非径向 DEA 模型之间的选择似乎取决于管理人员希望在效率得分排名分布中引入偏差的程度。径向模型似乎比非径向模型具有更低的偏差。对于包含两个或多个离散和连续变量的数据集，集成 DEA 模型似乎是性能最好的 DEA 模型。