Combining software quality predictive models: An evolutionary approach

During the past ten years, a large number of quality models have been proposed in the literature. In general, the goal of these models is to predict a quality factor starting from a set of direct measures. The lack of data behind these models makes it hard to generalize, to cross-validate, and to reuse existing models. As a consequence, for a company, selecting an appropriate quality model is a difficult, non-trivial decision. In this paper, we propose a general approach and a particular solution to this problem. The main idea is to combine and adapt existing models (experts) in such way that the combined model works well on the particular system or in the particular type of organization. In our particular solution, the experts are assumed to be decision tree or rule-based classifiers and the combination is done by a genetic algorithm. The result is a white-box model: for each software component, not only the model gives the prediction of the software quality factor, but it also provides the expert that was used to obtain the prediction. Test results indicate that the proposed model performs significantly better than individual experts in the pool.