Can metrics help to bridge the gap between the improvement of OO design quality and its automation?

Abstract

During the evolution of object-oriented (OO) systems, the preservation of a correct design should be a permanent quest. However, for systems involving a large number of classes and that are subject to frequent modifications, the detection and correction of design flaws may be a complex and resource-consuming task. The use of automatic detection and correction tools can be helpful for this task. Various works have proposed transformations that improve the quality of an OO system while preserving its behavior. In this paper, we investigate whether some OO metrics can be used as indicators for automatically detecting situations where a particular transformation can be applied to improve the quality of a system. The detection process is based on analyzing the impact of various transformations on these OO metrics using quality estimation models.