A replication study on the effects of weighted moving windows for software effort estimation
Sousuke Amasaki, Chris Lokan · 2016
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.