Andreas Ganser

On designing recommenders for graphical domain modeling environments

Recommender systems for source code artifacts are newly emerging and are now successfully supporting programmers. Their underlying knowledge bases, recommender algorithms, and user interfaces are well studied. Integrated into the development environment, they do a fairly good job in reducing complexity and development time.

A Framework for Model Recommenders

Content-assist systems and code completion are nicely accessible in integrated development environments (IDEs). Using multiple data sources and performing sophisticated completion in several editors is quite common. However, no such supporting system exists for modeling environments, e.g., a completion mechanism in class diagrams is only existent for textual items like names, if at all. We designed a framework to bolster model recommendation research and present the requirements, concepts, architecture, and the realization below. Last of which is easily extendable and adaptable to either new data recommendation strategies or new environments like editors. As additional tool support, we provide a simulation environment, which ease development as well as implementing recommendation algorithm. Accordingly, researchers get all the conceptual groundwork and a realized infrastructure that ease the initial burden to start recommendations in modeling environments.

Multi Back-Ends for a Model Library Abstraction Layer

Software development is moving in the direction of modeling as do quite a lot of other IT related tasks. This means, models become more and more important either as a means of communication or as parts of realizations. Unfortunately, these models are rarely reused which might be due to poor tool support.

Staged model evolution and proactive quality guidance for model libraries

AbstractA variety of modeling approaches, including model-driven development, consider model reuse as one of their cornerstones, but lack support for model reuse. This may be due to the available model repositories that barely exceed support for enhanced versioning or collaborative work and disregard model evolution. We believe that current model evolution approaches do not consider reuse sufficiently and that model repositories for reuse purposes should act as model libraries. This requires new functionality, because models for reuse need to achieve and maintain high quality. Moreover, quality assessment and assurance, which are tasks often considered tedious, need to be as simple as putting away or maintaining artifacts for reuse. In this study, we propose an approach for model evolution in UML model libraries that differs from general model evolution, since it is aimless and triggered by new external requirements. Our approach is founded on graphs that are partitioned into three stages with respect to the level of reusability. Each stage is defined by quality characteristics that are manifestations of a quality model consisting of four essential quality dimensions: syntactic, semantic, pragmatic, and emotional. In order to achieve the next level of reusability, i.e., change the stage of a model, a quality gate needs to be passed. This can be supported by a proactive approach that guides the modeler through the enhancement process and offers additional recommendations based on the level of reusability. Since guidance cannot be fully automated, we implement a review mechanism founded on the idea of the six thinking hats to help maintain focus on the main aspects of a review. Finally, our approach is enhanced to support the evolution of generations, i.e., a group of several model snapshots, to ease reusability.