Improving analogy-based software cost estimation by a resampling method

Estimation by analogy (EbA) is a well-known technique for software cost estimation. The popularity of the method is due to its straightforwardness and its intuitively appealing interpretation. However, in spite of the simplicity in application, the theoretical study of EbA is quite complicated. In this paper, we exploit the relation of EbA method to the nearest neighbor non-parametric regression in order to suggest a resampling procedure, known as iterated bagging, for reducing the prediction error. The improving effect of iterated bagging on EbA is validated using both artificial and real datasets from the literature, obtaining very promising results.