We present a metrics-based study of the evolution of Eclipse, an open source integrated development environment, based on data from seven major releases, from releases 1.0 to 3.3. We investigated whether three of the laws of software evolution were supported by the data. We found that Eclipse displayed continual change and growth, hence supporting laws 1 and 6. Six size indicators, out of eight, closely followed trend models. Four were linear and two superlinear. We found evidence of increasing complexity (law 2) in only two indicators, out of five. At subproject level, size and complexity are not distributed uniformly, and subproject size can be modelled as a negative exponential function of the rank position. We encountered a range of different size and complexity trends across subprojects. Our approach and results can help in evaluating the future evolution of Eclipse, the evolution of other systems and in performing comparisons.

Model-driven development of user interfaces has become increasingly powerful in recent years. Unfortunately, model-driven approaches have the inherent limitation that they cannot handle the informal nature of some of the artifacts used in truly multidisciplinary user interface development such as storyboards, sketches, scenarios and personas. In this chapter, we present an approach and tool support for multidisciplinary user interface development bridging informal and formal artifacts in the design and development process. Key features of the approach are the usage of annotated storyboards, which can be connected to other models through an underlying meta-model, and cross-toolkit design support based on an abstract user interface model.

Context-aware computing is a paradigm for governing the numerous mobile devices surrounding us. In this computing paradigm, software applications continuously and dynamically adapt to different "contexts" implying different software configurations of such devices. Unfortunately, modeling a context-aware application for all possible contexts is only feasible in the simplest of cases. Hence, tool support verifying certain properties is required. In this article, we introduce the Context-Aware Application model (CAA), in which context adaptations are specified explicitly as model transformations. By mapping this model to graphs and graph transformations, we can exploit graph transformation techniques such as critical pair analysis to find contexts for which the resulting application model is ambiguous. We validate our approach by means of an example of a mobile city guide, demonstrating that we can identify subtle context interactions that might go unnoticed otherwise.