MDEoptimiser

Burdusel, Alexandru; Zschaler, Steffen; Strüber, Daniel

doi:10.1145/3270112.3270130

Cited by 23 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abdeen et al [AVS + 14] follow this idea in their VIATRA-DSE framework, calling their approach rule-based design space exploration. Whereas VIATRA-DSE employs the VIATRA model transformation language to specify transformations, our comparison focuses on the MOMoT [BFT + 17] and MDEOptimiser [BZS18] tools, since they both use Henshin as the underlying transformation language.…”

Section: Ruled-based Encodingmentioning

confidence: 99%

“…This setup allows us to attribute any observed differences in performance to the used encoding. Specifically, we consider MOMoT [BFT + 17] and MDEOptimiser [BZS18], which follow the rule-based and the modelbased approach, respectively. Both are built atop of the EMF modeling platform [SBMP08], the Henshin model transformation language [ABJ + 10, SBG + 17], and the MOEA evolutionary search framework [MOE].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Searching for Optimal Models: Comparing Two Encoding Approaches.

John¹,

Burdusel²,

Bill³

et al. 2019

JOT

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Ruled-based Encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Searching for Optimal Models: Comparing Two Encoding Approaches.

John¹,

Burdusel²,

Bill³

et al. 2019

JOT

Self Cite

View full text Add to dashboard Cite

show abstract

Graph Consistency as a Graduated Property

Kosiol

Strüber

Taentzer

et al. 2020

Graph Transformation

Self Cite

View full text Add to dashboard Cite

Where graphs are used for modelling and specifying systems, consistency is an important concern. To be a valid model of a system, the graph structure must satisfy a number of constraints. To date, consistency has primarily been viewed as a binary property: a graph either is or is not consistent with respect to a set of graph constraints. This has enabled the definition of notions such as constraint-preserving and constraint-guaranteeing graph transformations. Many practical applications-for example model repair or evolutionary search-implicitly assume a more graduated notion of consistency, but without an explicit formalisation only limited analysis of these applications is possible. In this paper, we introduce an explicit notion of consistency as a graduated property, depending on the number of constraint violations in a graph. We present two new characterisations of transformations (and transformation rules) enabling reasoning about the gradual introduction of consistency: while consistency-sustaining transformations do not decrease the consistency level, consistency-improving transformations strictly reduce the number of constraint violations. We show how these new definitions refine the existing concepts of constraint-preserving and constraint-guaranteeing transformations. To support a static analysis based on our characterisations, we present criteria for deciding which form of consistency ensuring transformations is induced by the application of a transformation rule. We illustrate our contributions in the context of an example from search-based model engineering.

show abstract