Early software effort estimation is a hallmark of successful software project
management. Building a reliable effort estimation model usually requires
historical data. Unfortunately, since the information available at early stages
of software development is scarce, it is recommended to use software size
metrics as key cost factor of effort estimation. Use Case Points (UCP) is a
prominent size measure designed mainly for object-oriented projects.
Nevertheless, there are no established models that can translate UCP into its
corresponding effort, therefore, most models use productivity as a second cost
driver. The productivity in those models is usually guessed by experts and does
not depend on historical data, which makes it subject to uncertainty. Thus,
these models were not well examined using a large number of historical data. In
this paper, we designed a hybrid model that consists of classification and
prediction stages using a support vector machine and radial basis neural
networks. The proposed model was constructed over a large number of
observations collected from industrial and student projects. The proposed model
was compared against previous UCP prediction models. The validation and
empirical results demonstrated that the proposed model significantly surpasses
these models on all datasets. The main conclusion is that the environmental
factors of UCP can be used to classify and estimate productivity.

本研究提出了一种基于支持向量机和径向基神经网络的混合模型，该模型利用大量产业和学生项目的观测数据，通过将 UCP 的环境因素用于分类和估算生产率，显著优于以往的 UCP 预测模型，并证明了 UCP 可以作为早期软件开发估算成功的关键成本因素。