Thesis Abstract:
Model-driven engineering is a new approach in software development which uses models as the main artifact in the development process. If some changes are applied to models, other related artifacts should be changed as well. In other word, the changes should be propagated from models to other artifacts. In Model-driven engineering the changes are propagated through the transformations. The Transformation is a program which includes several rules. The simplest mode for change propagation is run all of the transformation rules on the whole elements of the model. This is called “batch transformation”. When the models contain a large number of elements, running the transformation would take a lot of time for applying changes and producing new artifacts. In order to reduce this time, it is necessary to prevent re-executing all transformation rules and to run partial transformation rules only for the changed elements in the model. This approach is called “incremental transformation”. The requirements for running the transformation incrementally are detecting the modified elements as well as the transformation rules that are related to those elements. There are exists several transformation languages; most of them run the batch transformation and don not support the incremental techniques. One of these languages is Epsilon transformation language (ETL). This language is one of the Epsilon family languages which is used for the model to model transformations. This language runs the batch transformation and it does not support incremental techniques.in this research, an approach is proposed to run ETL transformations incrementally. The approach detects the changed elements by using Epsilon Compare Language. It also detects the rules that are related to the changed elements, and run only these rules. this results in producing new elements in the target model or updating it if required. This approach has been implemented as an Eclipse-based. the results obtained from running the case study by using the provided approach represents the correctness of the transformation and reduce in the running time.