Performance Evaluation of Windowing Approach on Effort Estimation by Analogy

Background: In effort estimation model construction, it seems effective to window training project data so that only recently finished projects are used. This is because old projects might be less representative of an organization. The past study demonstrated windowing approach works with linear regression, which is one of global models. However, this approach has not been examined with local models. Local models use subset of historical data for model construction and thus windowing approach may influence on its performance more weakly. Aim: To investigate whether windowing approach works with local models. Method: We replicated the past study with EbA. Maxwell and CSC datasets were used for an experiment. Results: Windowing approach improved predictive performance. Although the difference was insignificant in any window size, the result indicated using windowing approach has positive effect on average. Conclusions: This result contributes to understand where windowing approach works well.