Predicting stability of classes in an object-oriented system

Abstract

The stability of a class in object-oriented system is one software quality characteristic that is important to assess at the early development stages. However, a direct measure of this software quality characteristic is not possible. Nonetheless, it can be predicted based on other measurable software attributes such as cohesion, coupling, and complexity. Many metrics have been proposed to assess these software attributes and for this purpose, prediction models have been widely used. However, in almost all cases, these models were not efficient when used to predict the quality characteristics (stability or other) of new unseen software as their prediction accuracy decreases significantly. In this paper, we present a heuristic approach that relies on the adaptation and recombination of already built predictive models to new unseen software.The predictive models are all rule-based models and the approach is tested on the stability of classes in an object-oriented software system. We compare our results to the machine learning algorithm C4.5, and we show that our approach out-beats it.