A UML/SPT Model Analysis Methodology for Concurrent Systems Based on Genetic Algorithms

Shousha, Marwa; Briand, Lionel C.; Labiche, Yvan

doi:10.1007/978-3-540-87875-9_34

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Our approach is inspired by contributions in SBSE [60][61][62][63][64]66]. As the name indicates, SBSE uses a search-based approach to solve optimization problems in software engineering.…”

Section: Search-based Software Engineeringmentioning

confidence: 99%

Search-based model transformation by example

et al. 2010

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Model transformation (MT) has become an important concern in software engineering. In addition to its role in model-driven development, it is useful in many other situations such as measurement, refactoring, and test-case generation. Roughly speaking, MT aims to derive a target model from a source model by following some rules or principles. So far, the contributions in MT have mostly relied on defining languages to express transformation rules. However, the task of defining, expressing, and maintaining these rules can be difficult, especially for proprietary and non-widely used formalisms. In some situations, companies have accumulated examples from past experiences. Our work starts from these observations to view the transformation problem as one to solve with fragmentary knowledge, i.e. with only examples of source-to-target MTs. Our approach has two main advantages: (1) it always proposes a transformation for a source model, even when rule induction is impossible or difficult to achieve; (2) it is independent from the source and target formalisms; aside from the examples, no extra information is needed. In this context, we propose an Communicated by Prof. Krzysztof Czarnecki.optimization-based approach that consists of finding in the examples combinations of transformation fragments that best cover the source model. To that end, we use two strategies based on two search-based algorithms: particle swarm optimization and simulated annealing. The results of validating our approach on industrial projects show that the obtained models are accurate.

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“…Our approach is inspired by contributions in SBSE [60][61][62][63][64]66]. As the name indicates, SBSE uses a search-based approach to solve optimization problems in software engineering.…”

Section: Search-based Software Engineeringmentioning

confidence: 99%

Search-based model transformation by example

et al. 2010

View full text Add to dashboard Cite

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“…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%

“…Global search techniques (like genetic algorithms or multiobjective optimization) have already proved to be successful in various MDE scenarios for finding constraints [16], model transformations [25] or solving static DSE problems [35] where an exhaustive search algorithm becomes infeasible. Moreover, they provide graceful degradation for problems where no solutions exist which meet all the objectives and constraints by relaxing hard constraints to soft constraints.…”

Section: Introductionmentioning

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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“…Shousha et al [18] present an approach to detect data races in UML models of concurrent programs. Our approach prevents data races entirely because SCOOP programs are data race free by design.…”

Section: Related Workmentioning

confidence: 99%

Concurrent Object-Oriented Development with Behavioral Design Patterns

Morandi

West

Nanz

et al. 2013

Software Architecture

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Abstract. Architectural modeling using the Unified Modeling Language (UML) can support the development of concurrent applications, but the problem of mapping the model to a concurrent implementation remains. This paper defines a scheme to map concurrent UML designs to a concurrent object-oriented program. Using the COMET method for the architectural design of concurrent object-oriented systems, each component and connector is annotated with a stereotype indicating its behavioral design pattern. For each of these patterns, a reference implementation is provided using SCOOP, a concurrent object-oriented programming model. Given the strong execution guarantees of the SCOOP model, which is free of data races by construction, this development method eliminates a source of intricate concurrent programming errors.

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A UML/SPT Model Analysis Methodology for Concurrent Systems Based on Genetic Algorithms

Cited by 12 publications

References 8 publications

Search-based model transformation by example

Search-based model transformation by example

Multi-objective optimization in rule-based design space exploration

Concurrent Object-Oriented Development with Behavioral Design Patterns

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