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Searching for optimal models: Comparing two encoding approaches

Journal article
Authors Stefan John
Alexandru Burdusel
Robert Bill
Daniel Strüber
Gabriele Taentzer
Steffen Zschaler
Manuel Wimmer
Published in Journal of Object Technology
Volume 18
Issue 3
Pages 1-22
ISSN 1660-1769
Publication year 2019
Published at Department of Computer Science and Engineering (GU)
Pages 1-22
Language en
Links dx.doi.org/10.5381/jot.2019.18.3.a6
Keywords Model-driven Engineering Search-based Software Engineering Optimization Encoding Comparative evaluation
Subject categories Software Engineering

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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