Three Studies on Model Transformations - Parsing, Generation and Ease of Use

ABSTRACTTransformations play an important part in both software development and the automatic processing of natural languages. We present three publications rooted in the multi-disciplinary research of Language Technology and Software Engineering and relate their contribution to the literature on syntactical transformations. Parsing Linear Context-Free Rewriting SystemsThe first publication describes four different parsing algorithms for the mildly context-sensitive grammar formalism Linear Context-Free Rewriting Systems. The algorithms automatically transform a text into a chart. As a result the parse chart contains the (possibly partial) analysis of the text according to a grammar with a lower level of abstraction than the original text. The uni-directional and endogenous transformations are described within the framework of parsing as deduction. Natural Language Generation from Class DiagramsUsing the framework of Model-Driven Architecture we generate natural language from class diagrams. The transformation is done in two steps. In the first step we transform the class diagram, defined by Executable and Translatable UML, to grammars specified by the Grammatical Framework. The grammars are then used to generate the desired text. Overall, the transformation is uni-directional, automatic and an example of a reverse engineering translation. Executable and Translatable UML - How Difficult Can it Be?Within Model-Driven Architecture there has been substantial research on the transformation from Platform-Independent Models (PIM) into Platform-Specifc Models, less so on the transformation from Computationally Independent Models (CIM) into PIMs. This publication reflects on the outcomes of letting novice software developers transform CIMs specified by UML into PIMs defined in Executable and Translatable UML.ConclusionThe three publications show how model transformations can be used within both Language Technology and Software Engineering to tackle the challenges of natural language processing and software development