Assessing and Improving the Quality of Model Transformations

Software is pervading our society more and more and is becoming increasingly complex. At the same time, software quality demands remain at the same, high level. Model-driven engineering (MDE) is a software engineering paradigm that aims at dealing with this increasing software complexity and improving productivity and quality. Models play a pivotal role in MDE. The purpose of using models is to raise the level of abstraction at which software is developed to a level where concepts of the domain in which the software has to be applied, i.e., the target domain, can be expressed effectively. For that purpose, domainspecific languages (DSLs) are employed. A DSL is a language with a narrow focus, i.e., it is aimed at providing abstractions specific to the target domain. This makes that the application of models developed using DSLs is typically restricted to describing concepts existing in that target domain. Reuse of models such that they can be applied for different purposes, e.g., analysis and code generation, is one of the challenges that should be solved by applying MDE. Therefore, model transformations are typically applied to transform domainspecific models to other (equivalent) models suitable for different purposes. A model transformation is a mapping from a set of source models to a set of target models defined as a set of transformation rules. MDE is gradually being adopted by industry. Since MDE is becoming more and more important, model transformations are becoming more prominent as well. Model transformations are in many ways similar to traditional software artifacts. Therefore, they need to adhere to similar quality standards as well. The central research question discoursed in this thesis is therefore as follows. How can the quality of model transformations be assessed and improved, in particular with respect to development and maintenance? Recall that model transformations facilitate reuse of models in a software development process. We have developed a model transformation that enables reuse of analysis models for code generation. The semantic domains of the source and target language of this model transformation are so far apart that straightforward transformation is impossible, i.e., a semantic gap has to be bridged. To deal with model transformations that have to bridge a semantic gap, the semantics of the source and target language as well as possible additional requirements should be well understood. When bridging a semantic gap is not straightforward, we recommend to address a simplified version of the source metamodel first. Finally, the requirements on the transformation may, if possible, be relaxed to enable automated model transformation. Model transformations that need to transform between models in different semantic domains are expected to be more complex than those that merely transform syntax. The complexity of a model transformation has consequences for its quality. Quality, in general, is a subjective concept. Therefore, quality can be defined in different ways. We defined it in the context of model transformation. A model transformation can either be considered as a transformation definition or as the process of transforming a source model to a target model.