  Use Case Points (UCP) method has been around for over two decades. Although,
there was a substantial criticism concerning the algebraic construction and
factors 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 no 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 the hypothetical
assumption between productivity and environmental factors is not necessarily a
requirement for success of locality.
