Implicit Incremental Model Analyses And Transformations

Implicit Incremental Model Analyses and Transformations

When models of a system change, analyses based on them have to be reevaluated in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. This thesis proposes multiple, combinable approaches and a new formalism based on category theory for implicitly incremental model analyses and transformations. The advantages of the implementation are validated using seven case studies, partially drawn from the Transformation Tool Contest (TTC).

Implicit Incremental Model Analyses and Transformations

Building Transformation Networks for Consistent Evolution of Interrelated Models

Complex software systems are described with multiple artifacts, such as code, design diagrams and others. Ensuring their consistency is crucial and can be automated with transformations for pairs of artifacts. We investigate how developers can combine independently developed and reusable transformations to networks that preserve consistency between more than two artifacts. We identify synchronization, compatibility and orchestration as central challenges, and we develop approaches to solve them.