Recovering model transformation traces using multi-objective optimization

Saada, Hajer; Huchard, Marianne; Nebut, Clémentine; Sahraoui, Houari

doi:10.1109/ase.2013.6693134

Cited by 16 publications

(12 citation statements)

References 10 publications

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“…Moreover, our approach uses exploration rules to guide the DSE process, while constraints are used to describe the requirements that must be fulfilled. Furthermore, unlike existing approaches which also used the NSGA-II (e.g., [7,8,21,31]), our approaches adapt the constraint-dominate strategy of NSGA-II so that constraints' fulfillment is described as a top-level soft optimization objective, rather than a reward/penalty parameter or as a hard selection criterion. Hence, our approach can even be applied to optimize an existing invalid solution with regard to the top-level objective, which is minimizing constraint violation.…”

Section: Related Workmentioning

confidence: 97%

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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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Section: Related Workmentioning

confidence: 97%

“…Multi-objective optimization techniques are widely used in Model Driven Engineering (MDE) field [13,16,31,35]. Recently, Kessentini et al [24] proposed an MDE-based framework for easing the adoption of search-based techniques (such as genetic algorithms) to MDE problems.…”

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

Self Cite

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“…For each trace, the target fragment (bottom) has been determined as corresponding to the source fragment (top). Transformation mappings can be identified by an expert during the design process or recovered (semi-)automatically using, for instance, approaches such as the ones proposed by Saada et al [2013] or Grammel et al [2012]. …”

Section: Model Transformation By Examplementioning

confidence: 99%

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Baki

Sahraoui

2016

ACM Trans. Softw. Eng. Methodol.

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Model-driven engineering promotes models as main development artifacts. As several models may be manipulated during the software-development life cycle, model transformations ensure their consistency by automating model generation and update tasks. However, writing model transformations requires much knowledge and effort that detract from their benefits. To address this issue, Model Transformation by Example (MTBE) aims to learn transformation programs from source and target model pairs supplied as examples. In this article, we tackle the fundamental issues that prevent the existing MTBE approaches from efficiently solving the problem of learning model transformations. We show that, when considering complex transformations, the search space is too large to be explored by naive search techniques. We propose an MTBE process to learn complex model transformations by considering three common requirements: element context and state dependencies and complex value derivation. Our process relies on two strategies to reduce the size of the search space and to better explore it, namely, multi-step learning and adaptive search. We experimentally evaluate our approach on seven model transformation problems. The learned transformation programs are able to produce perfect target models in three transformation cases, whereas precision and recall values larger than 90% are recorded for the four remaining cases. ACM Reference Format: Islem Baki and Houari Sahraoui. 2016. Multi-step learning and adaptive search for learning complex model transformations from examples.

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“…This approach is called model transformation by example (MTBE) and several dedicated approaches have been presented in the past. Because of the huge search space when searching for possible model transformations for a given set of input/output model pairs, search‐based techniques have been applied to automate this complex task . While MTBE approaches do not foresee the existence of model transformation rules, on the contrary, the goal is to produce such rules; we discussed in this paper the orthogonal problem of finding the best sequence of rule applications for a given set of transformation rules in combination with transformation goals.…”

Section: Related Workmentioning

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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Recovering model transformation traces using multi-objective optimization

Cited by 16 publications

References 10 publications

Multi-objective optimization in rule-based design space exploration

Multi-objective optimization in rule-based design space exploration

Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples

Search‐based model transformations

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