  Bellwether effect refers to the existence of exemplary projects (called the
Bellwether) within a historical dataset to be used for improved prediction
performance. Recent studies have shown an implicit assumption of using recently
completed projects (referred to as moving window) for improved prediction
accuracy. In this paper, we investigate the Bellwether effect on software
effort estimation accuracy using moving windows. The existence of the
Bellwether was empirically proven based on six postulations. We apply
statistical stratification and Markov chain methodology to select the
Bellwether moving window. The resulting Bellwether moving window is used to
predict the software effort of a new project. Empirical results show that
Bellwether effect exist in chronological datasets with a set of exemplary and
recently completed projects representing the Bellwether moving window. Result
from this study has shown that the use of Bellwether moving window with the
Gaussian weighting function significantly improve the prediction accuracy.
