<i>We’re Not Gonna Break It!</i> Consistency-Preserving Operators for Efficient Product Line Configuration

Horcas, José-Miguel; Strüber, Daniel; Burdusel, Alexandru; Martínez, Jabier; Zschaler, Steffen

doi:10.1109/tse.2022.3171404

Cited by 11 publications

(16 citation statements)

References 62 publications

(130 reference statements)

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“…Since our framework follows the model-based approach to MDO, we consider related work in more detail. We selected the following papers on the model-based approach to MDO that have been published in journals, at conferences, and at workshops on modeling and SBSE: [15,17,18,37,38,40,59,66]. We investigate which core concepts of MDO were considered.…”

Section: The Model-based Approach To Mdomentioning

confidence: 99%

“…Models are selected according to how well they meet the objectives or by giving preference to feasible solutions [15,38,59]. To terminate an evolutionary computation, various forms of termination conditions were presented in [15,37,38]: Simple conditions limit the number of evolutionary iterations performed in an evolutionary computation or the total runtime of these computations. Alternatively, the computation can be terminated if a certain number of iterations does not yield a sufficient improvement regarding the quality of solutions.…”

Section: The Model-based Approach To Mdomentioning

confidence: 99%

“…The generated operators were compared to manually designed operators; some effects on effectiveness and efficiency were noted. In [37], the authors use techniques from model-based MDO to tackle the problem of optimal configuration of product lines. Concretely, from a feature model with basic constraints they derive consistency-preserving configuration operators that transform valid configurations into valid configurations, i.e., they also construct sound operators.…”

Section: The Model-based Approach To Mdomentioning

confidence: 99%

“…MDO has been used in the literature to solve a variety of optimization problems [15,18,32,37,38,66] and the key ideas of the evolutionary algorithms presented are similar.…”

Section: A Graph-based Framework For Model-driven Optimizationmentioning

confidence: 99%

“…It is the task of future work to compare the efficiency of model-based encodings with traditional encodings and to translate models into more efficient encodings as needed. (An example where models are translated to bit strings is given in [37].) However, to show formal properties and implement evolutionary computation, we choose appropriate formal representations such as typed graphs for formalizing models (see below) and Ecore models for implementing them (in Sect.…”

Section: Computation Spacementioning

confidence: 99%

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A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties

et al. 2023

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Optimization problems in software engineering typically deal with structures as they occur in the design and maintenance of software systems. In model-driven optimization (MDO), domain-specific models are used to represent these structures while evolutionary algorithms are often used to solve optimization problems. However, designing appropriate models and evolutionary algorithms to represent and evolve structures is not always straightforward. Domain experts often need deep knowledge of how to configure an evolutionary algorithm. This makes the use of model-driven meta-heuristic search difficult and expensive. We present a graph-based framework for MDO that identifies and clarifies core concepts and relies on mutation operators to specify evolutionary change. This framework is intended to help domain experts develop and study evolutionary algorithms based on domain-specific models and operators. In addition, it can help in clarifying the critical factors for conducting reproducible experiments in MDO. Based on the framework, we are able to take a first step toward identifying and studying important properties of evolutionary operators in the context of MDO. As a showcase, we investigate the impact of soundness and completeness at the level of mutation operator sets on the effectiveness and efficiency of evolutionary algorithms.

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Section: The Model-based Approach To Mdomentioning

confidence: 99%

Section: The Model-based Approach To Mdomentioning

confidence: 99%

Section: The Model-based Approach To Mdomentioning

confidence: 99%

“…MDO has been used in the literature to solve a variety of optimization problems [15,18,32,37,38,66] and the key ideas of the evolutionary algorithms presented are similar.…”

Section: A Graph-based Framework For Model-driven Optimizationmentioning

confidence: 99%

Section: Computation Spacementioning

confidence: 99%

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A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties

et al. 2023

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Finding the Right Way to Rome: Effect-Oriented Graph Transformation

Kosiol

Strüber

Taentzer

et al. 2023

Lecture Notes in Computer Science

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Many applications of graph transformation require rules that change a graph without introducing new consistency violations. When designing such rules, it is natural to think about the desired outcome state, i.e., the desired effect, rather than the specific steps required to achieve it; these steps may vary depending on the specific rule-application context. Existing graph-transformation approaches either require a separate rule to be written for every possible application context or lack the ability to constrain the maximal change that a rule will create. We introduce effect-oriented graph transformation, shifting the semantics of a rule from specifying actions to representing the desired effect. A single effectoriented rule can encode a large number of induced classic rules. Which of the potential actions is executed depends on the application context; ultimately, all ways lead to Rome. If a graph element to be deleted (created) by a potential action is already absent (present), this action need not be performed because the desired outcome is already present. We formally define effect-oriented graph transformation, show how matches can be computed without explicitly enumerating all induced classic rules, and report on a prototypical implementation of effect-oriented graph transformation in Henshin.

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