Combining and adapting software quality predictive models by genetic algorithms

Object oriented (OO) design and programming have reached the maturity stage. OO software products are becoming more and more complex. Quality requirements are increasingly be-coming determining factors in selecting from design alternatives during software development. Therefore, it is important that the quality of the software be evaluated during the different stages of the development. 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 role of real software systems is crucial for building and/or validating these models. In most of the domains where predictive models are built (such as sociology, medicine, finance, and speech recognition) researchers are free to use large data repositories from which representative samples can be drawn. In the area of software engineering, however, such repositories are rare. The lack of data makes it hard to generalize, to cross-validate, and to reuse existing models. Since universal models do not exist, for a company, selecting an appropriate quality model is a difficult, non-trivial decision