UML sequence diagrams describe the interaction between objects of a system. In this paper, we present four approaches to analyze the quality of sequence diagrams. First, we propose formal model checking techniques and visualization to analyze the possible interaction traces and to identify ambiguities in a sequence diagram. Second, we identified patterns of common interactive behavior and we present an approach to identify these patterns. This approach is useful to identify implicit patterns and make them explicit. Third, we propose a series of checks to identify syntactic defects in sequence diagrams. The fourth approach consists of metrics describing properties such as coverage. The proposed approaches complement each other rather than being competing alternatives. We implemented these approaches in our SquAT tool and conducted a multi case study on three industrial and five research models to validate them. These models showed a large number of defects which were acknowledged by their designers.

Software development is becoming more and more model-centric. As a result models are used for a large variety of purposes, such as quality analysis, understanding, and maintenance. We argue that the UML and related existing tooling does not offer sufficient support to the developer to understand the models and evaluate their quality. We have proposed and implemented a collection of views to increase model understanding: MetaView, ContextView, MetricView, and UML-City-View. The purpose of this experiment is to validate whether there is a difference between the proposed views and the existing views with respect to comprehension correctness and comprehension effort. The comprehension task performed by the subjects was to answer a questionnaire about a model. 100 MSc students with relevant background knowledge have participated in the experiment. The results are statistically significant and show that the correctness is improved by 4.5% and that the time needed is reduced by 20%.

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.

The Unified Modeling Language (UML) is the de facto standard for designing and architecting software systems. UML offers a large number of diagram types that can be used with varying degree of rigour. As a result UML models may contain consistency defects. Previous research has shown that industrial UML models that are used as basis for implementation and maintenance contain large numbers of defects. This study investigates to what extent implementers detect defects and to what extent defects cause different interpretations by different readers. We performed two controlled experiments with a large group of students (111) and a group of industrial practitioners (48). The experiment's results show that defects often remain undetected and cause misinterpretations. We present a classification of defect types based on a ranking of detection rate and risk for misinterpretation. Additionally we observed effects of using domain knowledge to compensate defects. The results are generalizable to industrial UML users and can be used for improving quality assurance techniques for UML-based development.