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Searching for Optimal Models: Comparing Two Encoding Approaches

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

Standard

Searching for Optimal Models : Comparing Two Encoding Approaches. / John, Stefan; Burdusel, Alexandru; Bill, Robert; Struber, Daniel; Taentzer, Gabriele; Zschaler, Steffen; Wimmer, Manuel.

12th International Conference on Model Transformations ICMT 2019. Vol. 18 3. ed. 2019. p. 1-22 (Journal of Object Technology).

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

Harvard

John, S, Burdusel, A, Bill, R, Struber, D, Taentzer, G, Zschaler, S & Wimmer, M 2019, Searching for Optimal Models: Comparing Two Encoding Approaches. in 12th International Conference on Model Transformations ICMT 2019. 3 edn, vol. 18, Journal of Object Technology, pp. 1-22. https://doi.org/10.5381/jot.2019.18.3.a6

APA

John, S., Burdusel, A., Bill, R., Struber, D., Taentzer, G., Zschaler, S., & Wimmer, M. (2019). Searching for Optimal Models: Comparing Two Encoding Approaches. In 12th International Conference on Model Transformations ICMT 2019 (3 ed., Vol. 18, pp. 1-22). (Journal of Object Technology). https://doi.org/10.5381/jot.2019.18.3.a6

Vancouver

John S, Burdusel A, Bill R, Struber D, Taentzer G, Zschaler S et al. Searching for Optimal Models: Comparing Two Encoding Approaches. In 12th International Conference on Model Transformations ICMT 2019. 3 ed. Vol. 18. 2019. p. 1-22. (Journal of Object Technology). https://doi.org/10.5381/jot.2019.18.3.a6

Author

John, Stefan ; Burdusel, Alexandru ; Bill, Robert ; Struber, Daniel ; Taentzer, Gabriele ; Zschaler, Steffen ; Wimmer, Manuel. / Searching for Optimal Models : Comparing Two Encoding Approaches. 12th International Conference on Model Transformations ICMT 2019. Vol. 18 3. ed. 2019. pp. 1-22 (Journal of Object Technology).

Bibtex Download

@inbook{0fcb1cb943f643bab6215faf3e2ada51,
title = "Searching for Optimal Models: Comparing Two Encoding Approaches",
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.",
keywords = "Model-driven Engineering Search-based Software Engineering Optimization Encoding Comparative evaluation",
author = "Stefan John and Alexandru Burdusel and Robert Bill and Daniel Struber and Gabriele Taentzer and Steffen Zschaler and Manuel Wimmer",
year = "2019",
month = "6",
day = "14",
doi = "10.5381/jot.2019.18.3.a6",
language = "English",
volume = "18",
series = "Journal of Object Technology",
publisher = "Journal of Object Technology",
pages = "1--22",
booktitle = "12th International Conference on Model Transformations ICMT 2019",
edition = "3",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Searching for Optimal Models

T2 - Comparing Two Encoding Approaches

AU - John, Stefan

AU - Burdusel, Alexandru

AU - Bill, Robert

AU - Struber, Daniel

AU - Taentzer, Gabriele

AU - Zschaler, Steffen

AU - Wimmer, Manuel

PY - 2019/6/14

Y1 - 2019/6/14

N2 - 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.

AB - 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.

KW - Model-driven Engineering Search-based Software Engineering Optimization Encoding Comparative evaluation

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U2 - 10.5381/jot.2019.18.3.a6

DO - 10.5381/jot.2019.18.3.a6

M3 - Conference paper

VL - 18

T3 - Journal of Object Technology

SP - 1

EP - 22

BT - 12th International Conference on Model Transformations ICMT 2019

ER -

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