Use case points (UCP) method has been around for over two decades. Although there was a substantial criticism concerning the algebraic construction and factor assessment of UCP, it remains an efficient early size estimation method. Predicting software effort from UCP is still an ever-present challenge. The earlier version of UCP method suggested using productivity as a cost driver, where fixed or a few pre-defined productivity ratios have been widely agreed. While this approach was successful when not enough historical data is available, it is no longer acceptable because software projects are different in terms of development aspects. Therefore, it is better to understand the relationship between productivity and other UCP variables. This paper examines the impact of data locality approaches on productivity and effort prediction from multiple UCP variables. The environmental factors are used as partitioning factors to produce local homogeneous data either based on their influential levels or using clustering algorithms. Different machine learning methods, including solo and ensemble methods, are used to construct productivity and effort prediction models based on the local data. The results demonstrate that the prediction models that are created based on local data surpass models that use entire data. Also, the results show that conforming to the hypothetical assumption between productivity and environmental factors is not necessarily a requirement for the success of locality.

用例点（UCP）方法已经存在了二十多年。尽管对UCP的代数构造和因子评估存在很大的批评，但它仍然是一种有效的早期尺寸估计方法。从UCP预测软件工作量仍然是一个长期存在的挑战。UCP方法的早期版本建议使用生产率作为成本驱动因素，其中已广泛同意使用固定的或几个预定义的生产率比率。尽管在没有足够的历史数据的情况下这种方法是成功的，但由于软件项目在开发方面有所不同，因此不再可以接受。因此，最好了解生产率和其他UCP变量之间的关系。本文研究了数据局部化方法对来自多个UCP变量的生产率和工作量预测的影响。根据环境因素的影响程度或使用聚类算法，将环境因素用作划分因素以生成局部均质数据。不同的机器学习方法（包括独奏和合奏方法）用于基于本地数据构建生产力和工作量预测模型。结果表明，基于本地数据创建的预测模型优于使用整个数据的模型。此外，结果还表明，要想满足生产率和环境因素之间的假设假设，就不一定要成功实现本地化。根据环境因素的影响程度或使用聚类算法，将环境因素用作划分因素以生成局部均质数据。不同的机器学习方法（包括独奏和合奏方法）用于基于本地数据构建生产力和工作量预测模型。结果表明，基于本地数据创建的预测模型优于使用整个数据的模型。此外，结果还表明，要想满足生产率和环境因素之间的假设假设，就不一定要成功实现本地化。根据环境因素的影响程度或使用聚类算法，将环境因素用作划分因素以生成局部均质数据。不同的机器学习方法（包括独奏和合奏方法）用于基于本地数据构建生产力和工作量预测模型。结果表明，基于本地数据创建的预测模型优于使用整个数据的模型。此外，结果还表明，要想满足生产率和环境因素之间的假设假设，就不一定要成功实现本地化。用于基于本地数据构建生产力和工作量预测模型。结果表明，基于本地数据创建的预测模型优于使用整个数据的模型。此外，结果还表明，要想满足生产率和环境因素之间的假设假设，就不一定要成功实现本地化。用于基于本地数据构建生产力和工作量预测模型。结果表明，基于本地数据创建的预测模型优于使用整个数据的模型。此外，结果还表明，要想满足生产率和环境因素之间的假设假设，就不一定要成功实现本地化。