In this paper, we propose an approach for the combination and adaptation of software quality predictive models. Quality models are decomposed into sets of expertise. The approach can be seen as a search for a valuable set of expertise that when combined form a model with an optimal predictive accuracy. Since, in general; there will be several experts available and each expert will provide his expertise, the problem can be reformulated as an optimization and search problem in a large space of solutions. We present how the general problem of combining quality experts, modeled as Bayesian classifiers, can be tackled via a simulated annealing algorithm customization. The general approach was applied to build an expert predicting object-oriented software stability, a facet of software quality. Our findings demonstrate that, on available data, composed expert predictive accuracy outperforms the best available expert and it compares favorably with the expert build via a customized genetic algorithm.