Towards Scalable Search-Based Model Engineering with MDEOptimiser Scale

Burdusel, Alexandru; Zschaler, Steffen

doi:10.1109/models-c.2019.00032

Cited by 11 publications

(18 citation statements)

References 15 publications

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“…They propose the MOMoT (Marriage Optimization and Model Transformation) framework [36], which provides several algorithms for locally and globally searching transformation rules guided by single and multi-objective formulas. MDEOptimiser [16] is another tool for specifying and solving optimisation problems using MDE. In this tool, the user can specify optimization problems by using a Domain-Specific Language (DSL) and the tool can run evolutionary optimization algorithms that use models as an encoding for population members and model transformations as search operators.…”

Section: Search-based Software Engineeringmentioning

confidence: 99%

“…When applying well-known metaheuristic algorithms, typically an initial population is formed from a set of random models that conform to the metamodel. Then, the set of models is improved according to the defined fitness functions by update operations such as crossover and mutation that are usually implemented with model transformations [16]. Writing these model transformations requires mastery of a specific MT language, which is generally the ability of a modeler rather than a tester.…”

Section: Model Generation Using Metaheuristic Algorithmsmentioning

confidence: 99%

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Ant-colony optimization for automating test model generation in model transformation testing

Karimi,

Kolahdouz-Rahimi,

Troya

2024

Journal of Systems and Software

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Section: Search-based Software Engineeringmentioning

confidence: 99%

Section: Model Generation Using Metaheuristic Algorithmsmentioning

confidence: 99%

Ant-colony optimization for automating test model generation in model transformation testing

Karimi,

Kolahdouz-Rahimi,

Troya

2024

Journal of Systems and Software

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“…Hence, it would be important to create not only static repositories just containing data but interactive platforms where other modelers could store their datasets. In this direction, works like [68] could offer a common scalable infrastructure to reproduce and replicate experiments.…”

Section: Infrastructurementioning

confidence: 99%

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

2022

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Artificial intelligence has already proven to be a powerful tool to automate and improve how we deal with software development processes. The application of artificial intelligence to model-driven engineering projects is becoming more and more popular; however, within the model repair field, the use of this technique remains mostly an open challenge. In this paper, we explore some existing approaches in the field of AI-powered model repair. From the existing approaches in this field, we identify a series of challenges which the community needs to overcome. In addition, we present a number of research opportunities by taking inspiration from other fields which have successfully used artificial intelligence, such as code repair. Moreover, we discuss the connection between the existing approaches and the opportunities with the identified challenges. Finally, we present the outcomes of our experience of applying artificial intelligence to model repair.

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“…The tool can be used as an Eclipse plugin as well as in standalone mode using a command line interface. With the help of the Scale [10] experiments orchestration language, MDEO can be used to run large scale parallelised experiments using Amazon Web Services cloud infrastructure. The search algorithms supported by MDEO are implemented using MOEAFramework.…”

Section: Henshin Model Transformationsmentioning

confidence: 99%

Automatic generation of atomic multiplicity-preserving search operators for search-based model engineering

2021

Self Cite

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Recently, there has been increased interest in combining model-driven engineering and search-based software engineering. Such approaches use meta-heuristic search guided by search operators (model mutators and sometimes breeders) implemented as model transformations. The design of these operators can substantially impact the effectiveness and efficiency of the meta-heuristic search. Currently, designing search operators is left to the person specifying the optimisation problem. However, developing consistent and efficient search-operator rules requires not only domain expertise but also in-depth knowledge about optimisation, which makes the use of model-based meta-heuristic search challenging and expensive. In this paper, we propose a generalised approach to automatically generate atomic multiplicity-preserving search operators for a given optimisation problem. This reduces the effort required to specify an optimisation problem and shields optimisation users from the complexity of implementing efficient meta-heuristic search mutation operators. We evaluate our approach with a set of case studies and show that the automatically generated rules are comparable to, and in some cases better than, manually created rules at guiding evolutionary search towards near-optimal solutions.

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Towards Scalable Search-Based Model Engineering with MDEOptimiser Scale

Cited by 11 publications

References 15 publications

Ant-colony optimization for automating test model generation in model transformation testing

Ant-colony optimization for automating test model generation in model transformation testing

AI-powered model repair: an experience report—lessons learned, challenges, and opportunities

Automatic generation of atomic multiplicity-preserving search operators for search-based model engineering

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