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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.

Context: Recent studies have shown that estimation accuracy can be affected by only using a window of recent projects as training data for building an effort estimation model. The idea has been extended for regression-based estimation by weighting projects differently according to their order within the window. This significantly improved the accuracy of estimation in a single-company dataset from the ISBSG repository. Objective: To investigate the effects on estimation accuracy of using weighted moving windows with a new dataset, and compare results across datasets. Method: Using a dataset drawn from the Finnish dataset (studied previously with regard to windows but not with weighting), and using a fixed-size window policy, we examine the effect on estimation accuracy of using weighted moving windows. Results: The use of weighting functions could improve the estimation accuracy significantly, compared to using unweighted windows, with larger window sizes. The steepness of the weighting functions affects their effectiveness. However, in this dataset it is better to use a growing portfolio (retaining all past projects as training data) than to use windows. Conclusions:The results reinforce previous studies: the use of weighting functions can significantly improve the accuracy of regression-based estimation, compared to not using weighting, but in this dataset the use of moving windows reduces estimation accuracy.

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