On Applicability of Fixed-Size Moving Windows for ANN-based Effort Estimation

BACKGROUND: Several studies in software effort estimation have found that it can be effective to use a window of recent projects as training data for building an effort estimation model. The generality of the windowing approach still remains uncertain across the variety of effort estimation approaches that are based on different theory. Recent studies have focused on the use of windows with effort estimation models based on a machine learning approach, which could make better estimates than conventional linear regression. OBJECTIVE: To investigate the effect of using a window on estimation accuracy with a machine learning-based method, Artificial Neural Networks ( ANN). ANN was recently found as a popular and good performance method, and is based on a different theory from other Machine Learning-based methods used in past studies. METHOD: Using a single-company ISBSG dataset studied previously in similar research, we examine the effect of using a fixed-size windowing policy on the accuracy of estimates using ANN. RESULTS: There is a difference in the estimation accuracy between using a window and not using a window. Using windows of 50 to 120 projects reduced mean absolute errors by 5-7%. The effective range of window sizes was different from previous studies. CONCLUSIONS: Windowing significantly improves estimation accuracy with ANN. The results support past studies, in that the effective window sizes were different among estimation models. The results contribute to understanding characteristics of the windowing approach.