  Software effort estimation 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 five software engineering
datasets that have been used in the construction of software effort estimation
models. The kernel estimators are used in the generation of nonuniform weights
which are subsequently employed in weighted linear regression modeling. In each
model, older projects are assigned smaller weights while the more recently
completed projects are assigned larger weights, to reflect their potentially
greater relevance to present or future projects that need to be estimated.
Prediction errors are compared to those obtained from uniform models. Our
results indicate that, for the datasets that exhibit underlying nonstationary
processes, uniform models are more accurate than the nonuniform models; that
is, models based on kernel estimator functions are worse than the models where
no weighting was applied. In contrast, the accuracies of uniform and nonuniform
models for datasets that exhibited stationary processes were essentially
equivalent. Our analysis indicates that as the heterogeneity of a dataset
increases, the effect of stationarity is overridden. 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.
