Improving the quality of UML models in practice

Abstract

The importance of UML models in software engineering is increasing. Inherent to the UML is its lack of a formal semantics, its risk for inconsistency and completeness defects and the absence of modeling norms. These properties are sources for poor model quality and defects. To find out to which extent defects occur and what types of defects occur in practice we empirically investigate the state-of-the-practice of quality in UML models using a practitioners survey and a series of industrial case studies. Additionally we analyze the effects of defects in UML models experimentally. Based on this experiment we present an objective classification of UML defects which allows for prioritizing defects and thus allocate resources for defect removal. We aim at building a rule-set, metrics and visualization techniques to improve the quality of UML models during development. We propose a quality model that is specific for UML models. Finally, we propose modeling conventions, similar to coding conventions, to prevent for defects and to assure uniformity of modeling within an organization. We aim at empirically validating our techniques to provide pragmatic technology that can be transferred to industrial practice.