On the Automated Derivation of Domain-Specific UML Profiles





Professorship/Faculty: Fakultät Wirtschaftsinformatik und Angewandte Informatik: Abschlussarbeiten 
Authors: Kraas, Alexander
Publisher Information: Bamberg : University of Bamberg Press
Year of publication: 2019
Pages / Size: XVI, 359 Seiten : Illustrationen
ISBN: 978-3-86309-670-0
978-3-86309-671-7
Series ; Volume: Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg  ; 37
Supervisor(s): Lüttgen, Gerald  
Source/Other editions: Parallel erschienen als Druckausg. in der University of Bamberg Press, 2019 (29,50 EUR)
Language(s): English
Remark: 
Dissertation, Otto-Friedrich-Universität Bamberg, 2019
Link to order the print version: http://www.uni-bamberg.de/ubp/
DOI: 10.20378/irbo-54891
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-opus4-548916
Document Type: Doctoralthesis
Abstract: 
Similar to general-purpose languages, domain-specific languages (DSL) can be developed based on grammar formalisms, the model-driven engineering (MDE) is also becoming more and more important for the development of DSLs. On the one hand, metamodels can be used to define the syntax and semantics of DSLs. On the other hand, a DSL can be realized by adapting the Unified Modeling Language (UML) via the profiling mechanism, i.e., by defining a UML profile. For example, metamodels for a DSL can be created with the language concepts provided by the Meta Object Facility (MOF), distinguishing between the Essential MOF (EMOF) and the Complete MOF (CMOF). The latter variant is based on the EMOF but provides additional language concepts. A higher degree of abstraction and reuse of existing metamodels can be achieved by employing the language concepts provided by CMOF, which can be advantageous for the creation of more complex DSLs.

Apart from DSLs proposed in the literature, a model-based specification of DSLs becomes increasingly important for standardization. Both metamodels and UML profiles are often provided for standardized DSLs. The mappings of metamodels to UML profiles are usually only specified in some abstract manner by such standards. Therefore, such mappings do not provide all the details required to create executable model transformations. Moreover, the static semantics of metamodels and/or UML profiles are usually specified only in natural language. Thus, on the one hand, ambiguities can occur, and on the other hand, the soundness of the static semantics cannot be verified and validated without a manual translation into a machine-processable language.

The main goal of this dissertation is to remedy the identified weaknesses in developing DSLs, for which a CMOF-compliant metamodel and a UML profile shall be created. To achieve this goal, we propose a holistic MDE-based approach for automatically deriving UML profiles and model transformations based on CMOF metamodels. This approach enables an automatic transfer of DSL’s static semantics to UML profiles, so that the well-formedness of UML models with applied UML profile of a DSL can be verified automatically. In addition, the interoperability between UML and DSL models can already be validated in the development phase using the derived model transformations. Apart from new DSLs that are created from scratch, our approach also supports a migration of existing grammar-based DSLs towards CMOF-based metamodels, provided syntax rules exist.

To verify and evaluate the presented derivation approach, we have implemented a toolchain that we used to conduct two case studies on the Specification and Description Language (SDL) and the Test Description Language (TDL).
SWD Keywords: Modellierung ; Object constraint language ; UML ; Profil ; Herkunft ; Semantik
Keywords: Metamodeling, UML profiles, derivation, static semantics, OCL
DDC Classification: 004 Computer science 
RVK Classification: ST 230   
URI: https://fis.uni-bamberg.de/handle/uniba/45648
Release Date: 27. May 2019

File SizeFormat  
SWIAI37KraasAlexanderopusk1se_A3a.pdf5.8 MBAdobe PDFView/Open