Rule-based Graph Repair

Sandmann, Christian; Habel, Annegret

doi:10.4204/eptcs.309.5

Cited by 9 publications

(18 citation statements)

References 20 publications

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“…In [12], Habel and Sandmann derive graph programs from graph constraints of nesting level ≤ 2. In [25], they extend their results to constraints in ANF which end with ∃C or constraints of one of the forms ∃(C, ¬∃C ) or ¬∃C. They also investigate whether a given set of rules allows to repair such a given constraint.…”

Section: Related Workmentioning

confidence: 96%

“…For example, applying moveFeature can leave behind an empty class, thus violating c 2 . While constraint violations can potentially be removed using repair techniques [12,22,25], these can be computationally expensive and may involve strategies that lead to certain regions of the search space being preferred, threatening the efficiency of the search. Instead, it would be desirable to design mutation operators that impact consistency in a positive or at least neutral way.…”

Section: Examplementioning

confidence: 99%

“…We define a normal form for graph conditions that requires that the occurring quantifiers alternate. For every linear condition there is an equivalent condition in this normal form [25,Fact 2].…”

Section: Definition 1 ((Nested) Graph Conditions and Constraintsmentioning

confidence: 99%

“…Any consistency-improving rule can be turned into a strongly consistencyimproving rule if suitable pre-conditions can be added that restrict the applicability of the rule only to those cases where it can actually improve a constraint violation. This links the two forms of consistency-improving rules to their practical applications: in model repair [21,25] we want to use rules that will only make a change to a graph when there is a violation to be repaired-strongly consistency-improving rules. However, in evolutionary search [5], we want to allow rules to be able to make changes even when there is no need for repair, but to fix violations when they occur; consistency-improving rules are well-suited here as they can be applied even when no constraint violations need fixing.…”

Section: Consistency Sustainment and Improvementmentioning

confidence: 99%

“…For example, research in model repair has aimed to automatically produce graph-transformation rules that will gradually improve the consistency of a given graph. Such a rule may not make a graph completely consistent in one transformation step, but performing a sequence of such transformations will eventually produce a consistent graph (e.g., [12,21,22,25]). In the area of search-based model engineering (e.g., [5,9]), rules are required to be applicable to inconsistent graphs and, at least, not to produce new inconsistencies.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Graph Consistency as a Graduated Property

Kosiol

Strüber

Taentzer

et al. 2020

Graph Transformation

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Where graphs are used for modelling and specifying systems, consistency is an important concern. To be a valid model of a system, the graph structure must satisfy a number of constraints. To date, consistency has primarily been viewed as a binary property: a graph either is or is not consistent with respect to a set of graph constraints. This has enabled the definition of notions such as constraint-preserving and constraint-guaranteeing graph transformations. Many practical applications-for example model repair or evolutionary search-implicitly assume a more graduated notion of consistency, but without an explicit formalisation only limited analysis of these applications is possible. In this paper, we introduce an explicit notion of consistency as a graduated property, depending on the number of constraint violations in a graph. We present two new characterisations of transformations (and transformation rules) enabling reasoning about the gradual introduction of consistency: while consistency-sustaining transformations do not decrease the consistency level, consistency-improving transformations strictly reduce the number of constraint violations. We show how these new definitions refine the existing concepts of constraint-preserving and constraint-guaranteeing transformations. To support a static analysis based on our characterisations, we present criteria for deciding which form of consistency ensuring transformations is induced by the application of a transformation rule. We illustrate our contributions in the context of an example from search-based model engineering.

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Section: Related Workmentioning

confidence: 96%

Section: Examplementioning

confidence: 99%

Section: Definition 1 ((Nested) Graph Conditions and Constraintsmentioning

confidence: 99%

Section: Consistency Sustainment and Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph Consistency as a Graduated Property

Kosiol

Strüber

Taentzer

et al. 2020

Graph Transformation

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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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