Design-Space Exploration through Constraint-Based Model-Transformation

Schätz, Bernhard; Hölzl, Florian; Lundkvist, Torbjörn

doi:10.1109/ecbs.2010.25

Cited by 27 publications

(10 citation statements)

References 11 publications

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“…However, due to the complexity of the problems we are dealing with, that is, problems that warrant a meta‐heuristic search, a manual approach is clearly not feasible for larger examples. A search‐and‐check‐approach must be able to take back applied transformations in form of backtracking and in most cases more than one possible solution exists, as also observed by Schätz et al . Therefore, we would at least need to provide rules which are able to negate the effects of another rule.…”

Section: Resultsmentioning

confidence: 99%

Search‐based model transformations

Fleck

Troya

Wimmer

2016

J Software Evolu Process

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Model transformations are an important cornerstone of model‐driven engineering, a discipline which facilitates the abstraction of relevant information of a system as models. The success of the final system mainly depends on the optimization of these models through model transformations. Currently, the application of transformations is realized either by following the apply‐as‐long‐as‐possible strategy or by the provision of explicit rule orchestrations. This implies two main limitations. First, the optimization objectives are implicitly hidden in the transformation rules and their orchestration. Second, manually finding the best orchestration for a particular scenario is a major challenge due to the high number of possible combinations. To overcome these limitations, we present a novel framework that builds on the non‐intrusive integration of optimization and model transformation technologies. In particular, we formulate the transformation orchestration task as an optimization problem, which allows for the efficient exploration of the transformation space and explication of the transformation objectives. Our generic framework provides several search algorithms and guides the user in providing a proper search configuration. We present different instantiations of our framework to demonstrate its feasibility, applicability, and benefits using several case studies. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

Search‐based model transformations

Fleck

Troya

Wimmer

2016

J Software Evolu Process

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“…In this way, more than one valid alternative may be proposed as output (non-determinism), leaving the designers the choice for a suitable solution. This choice may have a negative impact on software cost and quality [20].…”

Section: Uncertainty In Generated Modelsmentioning

confidence: 99%

Uncertainty in bidirectional transformations

Eramo

Pierantonio

Rosa

2014

Proceedings of the 6th International Workshop on Modeling in Software Engineering

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In Model-Driven Engineering, models are primary artifact manipulated by means of automated transformations. Recently, a notion of uncertainty has been introduced in models permitting modelers to postpone design decisions in case of lack of information. Interestingly, other forms of model uncertainty are induced by bidirectional transformations. In fact, in certain situations more than one admissible solution is in principle possible, despite most of the current languages generate only one model at time, possibly not the desired one.In this paper, the uncertainty due to the solution multiplicity in bidirectional transformations is discussed. In particular, we propose to represent the models in the solution space as concretizations of an uncertain model because there are cases where the responsibility of identifying the solution must be left to the modeler. The problem is illustrated by a round-tripping scenario realized with the JTL transformation language.

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“…Schätz et al [33] developed an interactive, incremental process using declarative transformation rules for driving the exploration. The rules are modified interactively (user guided) to improve the performance of the exploration, while our approach uses genetic algorithms to guide the mutation and crossover operations to find solution models.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective optimization in rule-based design space exploration

Abdeen

Varró

Sahraoui

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Design space exploration (DSE) aims to find optimal design candidates of a domain with respect to different objectives where design candidates are constrained by complex structural and numerical restrictions. 14,18] aims to find such candidates that are reachable from an initial model by applying a sequence of exploration rules. Solving a rule-based DSE problem is a difficult challenge due to the inherently dynamic nature of the problem.In the current paper, we propose to integrate multi-objective optimization techniques by using Non-dominated Sorting Genetic Algorithms (NSGA) to drive rule-based design space exploration. For this purpose, finite populations of the most promising design candidates are maintained wrt. different optimization criteria. In our context, individuals of a generation are defined as a sequence of rule applications leading from an initial model to a candidate model. Populations evolve by mutation and crossover operations which manipulate (change, extend or combine) rule execution sequences to yield new individuals.Our multi-objective optimization approach for rule-based DSE is domain independent and it is automated by tooling built on the Eclipse framework. The main added value is to seamlessly lift multi-objective optimization techniques to the exploration process preserving both domain independence and a high-level of abstraction. Design candidates will still be represented as models and the evolution of these models as rule execution sequences. Constraints are captured by model queries while objectives can be derived both from models or rule applications. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Design-Space Exploration through Constraint-Based Model-Transformation

Cited by 27 publications

References 11 publications

Search‐based model transformations

Search‐based model transformations

Uncertainty in bidirectional transformations

Multi-objective optimization in rule-based design space exploration

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