The Effects of Moving Windows to Software Estimation: Comparative Study on Linear Regression and Estimation by Analogy

BACKGROUND: Models for estimating software development effort are constructed using a set of historical data for training. In construction, it seems effective to use a window of training data that consists of only recently finished projects. Two previous studies evaluated the use of a window with linear regression (LR) and estimation by analogy (EbA). However, these studies were based on different datasets and thus their findings could not be compared directly. OBJECTIVE: This study investigates the effect of using a window on estimation accuracy with EbA and LR. The difference between the results with the two modeling techniques was also investigated. METHOD: We compared the effectiveness of using a window with both LR and EbA, with the same experiment settings and data. RESULTS: There is a difference in accuracy between using a window and not using a window, with both software estimation methods. However, the effect of the use of a window is weaker with EbA than with LR. CONCLUSIONS: Windowing is effective with EbA and LR. However, the degree of effectiveness is weaker with EbA than with LR. The results contribute to understand how the windowing approach interrelates with software estimation models.