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Searching for Optimal Models: Comparing Two Encoding Approaches

Research output: Chapter in Book/Report/Conference proceedingConference paper

Stefan John, Alexandru Burdusel, Robert Bill, Daniel Struber, Gabriele Taentzer, Steffen Zschaler, Manuel Wimmer

Original languageEnglish
Title of host publication12th International Conference on Model Transformations ICMT 2019
Pages1-22
Number of pages22
Volume18
Edition3
DOIs
Publication statusE-pub ahead of print - 14 Jun 2019

Publication series

NameJournal of Object Technology
PublisherJournal of Object Technology
ISSN (Print)1660-1769

Documents

King's Authors

Abstract

Search-Based Software Engineering (SBSE) is about solving software development problems by formulating them as optimization problems. In the last years, combining SBSE and Model-Driven Engineering (MDE), where models and model transformations are treated as key artifacts in the development of complex systems, has become increasingly popular. While search-based techniques have often successfully been applied to tackle MDE problems, a recent line of research investigates how a model-driven design can make optimization more easily accessible to a wider audience. In previous model-driven optimization efforts, a major design decision concerns the way in which solutions are encoded. Two main options have been explored: a model-based encoding representing candidate solutions as models, and a rule-based encoding representing them as sequences of transformation rule applications. While both encodings have been applied to different use cases, no study has yet compared them systematically. To close this gap, we evaluate both approaches on a common set of optimization problems, investigating their impact on the optimization performance. Additionally, we discuss their differences, strengths, and weaknesses laying the foundation for a knowledgeable choice of the right encoding for the right problem.

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