Qualitative Evaluation of N-Way Model Matching Approaches
- In product line engineering tasks, the need for merging models from different product variants emerges as the commonly used clone-and-own approach suffers from high maintenance costs in the long run. By identifying models with a high number of similarities we can merge them to one highly reusable model. This approach will increase the maintainability, and further expandability of the model. Already many works have been published aiming to solve this problem with different N-way model Matching approaches. However, there is lack of practical evidence that the published theories work as designed in real world cases. In this work, we will evaluate relevant published approaches and then attempt to integrate the most promising one in the product line analysis framework VARIOUS from Fraunhofer IESE. Next, the implemented approach will be evaluated in comparison to the existing mechanism for model matching that VARIOUS integrates that is called "System Aligner". The main aspects of our evaluation are: • Accuracy - Can it accurately find the most similar models? • Performance - How fast is it? • Scalability - How well does it scale in large amount of input models? • Configurability - Can it be adapted easily for different systems?
Verfasser*innenangaben: | Dimitrios Volikakis |
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URN: | urn:nbn:de:hbz:386-kluedo-74342 |
Betreuer*in: | Vasil Tenev |
Dokumentart: | Masterarbeit |
Sprache der Veröffentlichung: | Englisch |
Datum der Veröffentlichung (online): | 27.09.2023 |
Jahr der Erstveröffentlichung: | 2023 |
Veröffentlichende Institution: | Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau |
Titel verleihende Institution: | Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau |
Datum der Publikation (Server): | 28.09.2023 |
Seitenzahl: | IX, 57 |
Fachbereiche / Organisatorische Einheiten: | Distance and Independent Studies Center (DISC) |
DDC-Sachgruppen: | 0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
Sammlungen: | Herausragende Masterarbeiten am DISC |
Lizenz (Deutsch): | Creative Commons 4.0 - Namensnennung (CC BY 4.0) |