Software development effort estimation is the process of predicting the effort required to develop a software system. In order to improve the estimation accuracy, many different models have been proposed in the literature. Multiple classification systems represent an important field of research for machine learning. In order to estimate software development effort, this paper proposes a heterogeneous and dynamic ensemble selection model, composed by a set of regressors dynamically selected by classifiers. Along with the proposed method it is conducted an experimental analysis involving a relevant set of software effort estimation problems, which has led to better results than those achieved by classical and state of the art models previously presented.