S. Amasaki and C. Lokan
On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation Authors: S. Amasaki and C. Lokan mini-abst(80words): It seems effective to use a window of training data so that an effort estimation 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. We examined the effects of weighted moving windows on effort estimation accuracy. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, though the methods also significantly worsened accuracy in smaller windows.