An Evaluation of Model Transformation Languages for UML Quality Engineering

Detecting modeling errors in the first stages of a software development process can spare time and money. Software quality engineering is a field of computer science for evaluating the quality of software and providing mechanisms to ensure software quality. This thesis evaluates the transformation languages ATLAS Transformation Language (ATL), Epsilon Transformation Language (ETL), Query/View/Transformation (QVT), and Xtend by ana- lyzing their characteristics in relation to the International Organization for Standardization (ISO) 9126 standard, a language characteristics taxonomy proposed by Czarnecki and Helsen, and their applicability for calculating metrics and detecting bad smells in Unified Modeling Language (UML) models. A case study has been used to evaluate the transfor- mation languages in the task of executing a Model to Model (M2M) transformation for cal- culating metrics and detecting bad smells. All four transformation languages are suitable for UML quality engineering, however there are differences, such as performance issues or tooling characteristics that should be taken into consideration.