Sustaining and improving graduated graph consistency: A static analysis of graph transformations

Kosiol, Jens; Strüber, Daniel; Taentzer, Gabriele; Zschaler, Steffen

doi:10.1016/j.scico.2021.102729

Cited by 14 publications

(14 citation statements)

References 35 publications

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“…A first definition of such a graduated notion of consistency of graphs was given in [19]. The following definitions extend the formalization from [19] in such a way that it is not only possible to count violations but also to distinguish their severity.…”

Section: Consistency Checksmentioning

confidence: 99%

“…In the following, we introduce two special forms of graph transformation that are related to the consistency of a given constraint. Our definitions are based on the number of violations introduced above and refine the notions of graduated consistency that have been introduced in[19] (we provide a more detailed, formal comparison in Section 6).Definition 5.3 (Consistency-increasing and consistency-maintaining transformation). Given a constraint c, a transformation t : G =⇒ H is called consistency maintaining (c-maintaining) w.r.t.…”

mentioning

confidence: 99%

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Empowering model repair: A rule-based approach to graph repair without side effects -- Extended Version

Lauer,

Kosiol,

Taentzer

2024

Preprint

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Working with models can lead to inconsistencies due to erroneous or contradictory actions during concurrent modeling processes. Modern modeling environments typically tolerate inconsistencies and support their detection. However, at a later stage of development, models are expected to be consistent, which means that their inconsistencies should be considered and resolved. The process of resolving model inconsistencies is commonly referred to as model repair. Our approach to model repair is semi-automatic in the sense that the system computes appropriate repair plans and the modeler decides which path to take. The speciality of our approach is that the repair process can register any small improvement in the model. This allows the interaction with the user to be optimized, resulting in an approach with a high level of automation on the one hand and flexible configuration options on the other. The approach is able to provide repair plans that do not have side effects, i.e., the computed repair plans do not inadvertently introduce a new inconsistency of already repaired constraints into the model. Since models often have a graph-like structure, we present our approach to model repair based on graphs. Our approach is completely formal and uses the algebraic graph transformation approach to prove its correctness.

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Section: Consistency Checksmentioning

confidence: 99%

mentioning

confidence: 99%

Empowering model repair: A rule-based approach to graph repair without side effects -- Extended Version

Lauer,

Kosiol,

Taentzer

2024

Preprint

Self Cite

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show abstract

“…In a line of research on combining model transformation with searchbased software engineering [52], [53], [54], several works deal with validity during generation and analysis of transformation rules. Kosiol et al [55] support a consistency notion and associated analysis to reason about the validity impact of a particular rule; however, they do not consider rule generation.…”

Section: Related Workmentioning

confidence: 99%

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

Horcas¹,

Strüber

Burdusel

et al. 2023

IIEEE Trans. Software Eng.

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“…For this, one must additionally ensure that an element mutation operator cannot introduce violations of the remaining language constraints, i.e., for constraints from LC \ PC. There are several approaches to (semi-)automatically check whether a transformation rule preserves a given graph constraint [9,41,50,51,53]. If a constraint is first-order, it can be expressed as a nested graph constraint and then ensured by integrating it as an application condition into a transformation rule [33,54]; the resulting rule forms an element mutation operator that preserves the given constraint.…”

Section: Evolutionary Operatorsmentioning

confidence: 99%

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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Sustaining and improving graduated graph consistency: A static analysis of graph transformations

Cited by 14 publications

References 35 publications

Empowering model repair: A rule-based approach to graph repair without side effects -- Extended Version

Empowering model repair: A rule-based approach to graph repair without side effects -- Extended Version

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

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

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