The object-oriented (OO) paradigm has now reached maturity. OO software products are becoming more complex which makes their evolution effort and time consuming. In this respect, it has become important to develop tools that allow assessing the stability of OO software (i.e., the ease with which a software item can evolve while preserving its design). In general, predicting the quality of OO software is a complex task. Although many predictive models are proposed in the literature, we remain far from having reliable tools that can be applied to real industrial systems. The main obstacle for building reliable predictive tools for real industrial systems is the lackof representative samples. Unlike other domains where such samples can be drawn from available large repositories of data, in OO software the lack of such repositories makes it hard to generalize, to validate and to reuse existing models. Since universal models do not exist, selecting an appropriate quality model is a difficult, non-trivial decision for a company. In this paper, we propose two general approaches to solve this problem. They consist of combining/adapting a set of existing models. The process is driven by the context of the target company. These approaches are applied to OO software stability prediction.
['Salah Bouktif']
Improving Rule Set Based Software Quality Prediction: A Genetic Algorithm-based Approach.