  Software effort estimation (SEE) models are typically developed based on an
underlying assumption that all data points are equally relevant to the
prediction of effort for future projects. The dynamic nature of several aspects
of the software engineering process could mean that this assumption does not
hold in at least some cases. This study employs three kernel estimator
functions to test the stationarity assumption in three software engineering
datasets that have been used in the construction of software effort estimation
models. The kernel estimators are used in the generation of non-uniform weights
which are subsequently employed in weighted linear regression modeling.
Prediction errors are compared to those obtained from uniform models. Our
results indicate that, for datasets that exhibit underlying non-stationary
processes, uniform models are more accurate than non-uniform models. In
contrast, the accuracy of uniform and non-uniform models for datasets that
exhibited stationary processes was essentially equivalent. The results of our
study also confirm prior findings that the accuracy of effort estimation models
is independent of the type of kernel estimator function used in model
development.
