As society becomes ever more dependent on computer systems, there is more and more pressure on development teams to produce high-quality software. Many companies therefore rely on quality models, program suites that analyse and evaluate the quality of other programs, but building good quality models is hard as there are many questions concerning quality modelling that have yet to be adequately addressed in the literature. We analysed quality modelling practices in a large organisation and identified three dimensions where research is needed: proper support of the subjective notion of quality, techniques to track the quality of evolving software, and the composition of quality judgments from different abstraction levels.;To support software evolution, we consider scores produced by quality models as signals and the use of signal data-mining techniques to identify patterns in the evolution of quality. We studied how anti-patterns are introduced and removed from systems.;Software is typically written using a hierarchy of components, yet quality models do not explicitly consider this hierarchy. As the last part of our dissertation, we present two level quality models. These are composed of three parts: a component-level model, a secondmodel to evaluate the importance of each component, and a container-level-model to combine the contribution of components with container attributes. This approach was tested on the prediction of class-level changes based on the quality and importance of its components: methods. It was shown to be more useful than single-level, traditional approaches.;To finish, we reapplied this two-level methodology to the problem of assessing web site navigability. Our models could successfully distinguish award-winning sites from average sites picked at random.;To tackle subjectivity, we propose using Bayesian models as these can deal with uncertain data. We applied our models to the problem of anti-pattern detection. In a study of two open-source systems, we found that our approach was superior to state of the art rule-based techniques.;Throughout the dissertation, we present not only theoretical problems and solutions, but we performed experiments to illustrate the pros and cons of our solutions. Our results show that there are considerable improvements to be had in all three proposed dimensions. In particular, our work on quality composition and importance modelling is the first that focuses on this particular problem. We believe that our general two-level models are only a starting point for more in-depth research.;Keywords : Software engineering, software quality, quality models, empirical studies, Bayesian models, software evolution, quality composition.
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