On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation. Issue 7 (20th August 2014) - Amasaki, S.; Lokan, C.; Heidrich, Jens; Oivo, Markku; Jedlitschka, Andreas
Abstract In construction of an effort estimation model, it seems effective to use a window of training data so that the model is trained with only recent projects. Considering the chronological order of projects within the window, and weighting projects according to their order within the window, may also affect estimation accuracy. In this study, we examined the effects of weighted moving windows on effort estimation accuracy. We compared weighted and non‐weighted moving windows under the same experimental settings. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, although the methods also significantly worsened accuracy in smaller windows. This result contributes to understanding properties of moving windows. Copyright © 2014 John Wiley & Sons, Ltd.