Incremental (Unidirectional) Model Transformation with eMoflon::IBeX

Weidmann, Nils B.; Anjorin, Anthony; Robrecht, Patrick; Varró, Gergely

doi:10.1007/978-3-030-23611-3_8

Cited by 5 publications

(8 citation statements)

References 12 publications

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“…Examples of such languages are detailed and compared in Anjorin, Buchmann, Westfechtel, et al [18] or [53]. Some languages like eMoflon Leblebici et al [30], NMF Synchronizations [45], or Viatra [54] extend this further by providing the ability to perform incremental transformations both being features that are hard to reproduce in general purpose languages in our experience. Even ATL now has several extensions allowing it to run incremental transformations [55,56].…”

Section: Limits Of Our Results In the Context Of The Research Fieldmentioning

confidence: 98%

“…This measure supplements LOC because it is less influenced by code style and independent from keyword and method name size [18]. This method for calculating transformation code size has already been successfully used by Anjorin, Buchmann, Westfechtel, et al [18] to compare several (bidirectional) transformation languages including eMoflon [30], JTL [31], NMF Synchronizations [32] and their own language BXtend [33]. Their argument for using word count is that because it approximates the number of lexical units it more accurately measures the size of a solution than lines of code.…”

Section: Rq1: How Much Can the Complexity And Size Of Transformations Written In Java Se14 Be Improved Compared To Java Se5?mentioning

confidence: 99%

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Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

2021

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Model transformations are among the key concepts of model-driven engineering (MDE), and dedicated model transformation languages (MTLs) emerged with the popularity of the MDE pssaradigm about 15 to 20 years ago. MTLs claim to increase the ease of development of model transformations by abstracting from recurring transformation aspects and hiding complex semantics behind a simple and intuitive syntax. Nonetheless, MTLs are rarely adopted in practice, there is still no empirical evidence for the claim of easier development, and the argument of abstraction deserves a fresh look in the light of modern general purpose languages (GPLs) which have undergone a significant evolution in the last two decades. In this paper, we report about a study in which we compare the complexity and size of model transformations written in three different languages, namely (i) the Atlas Transformation Language (ATL), (ii) Java SE5 (2004–2009), and (iii) Java SE14 (2020); the Java transformations are derived from an ATL specification using a translation schema we developed for our study. In a nutshell, we found that some of the new features in Java SE14 compared to Java SE5 help to significantly reduce the complexity of transformations written in Java by as much as 45%. At the same time, however, the relative amount of complexity that stems from aspects that ATL can hide from the developer, which is about 40% of the total complexity, stays about the same. Furthermore we discovered that while transformation code in Java SE14 requires up to 25% less lines of code, the number of words written in both versions stays about the same. And while the written number of words stays about the same their distribution throughout the code changes significantly. Based on these results, we discuss the concrete advancements in newer Java versions. We also discuss to which extent new language advancements justify writing transformations in a general purpose language rather than a dedicated transformation language. We further indicate potential avenues for future research on the comparison of MTLs and GPLs in a model transformation context.

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Section: Limits Of Our Results In the Context Of The Research Fieldmentioning

confidence: 98%

Section: Rq1: How Much Can the Complexity And Size Of Transformations Written In Java Se14 Be Improved Compared To Java Se5?mentioning

confidence: 99%

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

2021

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“…Specifically, the notions of a delta lens with amendments (in both asymmetric and symmetric variants) was defined in [8], and several composition results were proved. Another extensive body or work within the delta-based area is modelling and implementing model transformations with triple-graph grammars (TGG) [19,20]. TGG provides an implementation frame-work for delta lenses as is shown and discussed in [21,22,23], and thus inevitably consider change propagation on a much more concrete level than lenses.…”

Section: Related Workmentioning

confidence: 99%

General Supervised Learning as Change Propagation with Delta Lenses

Diskin¹

2020

Lecture Notes in Computer Science

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Delta lenses are an established mathematical framework for modelling and designing bidirectional model transformations (Bx). Following the recent observations by Fong et al, the paper extends the delta lens framework with a a new ingredient: learning over a parameterized space of model transformations seen as functors. We will define a notion of an asymmetric learning delta lens with amendment (ala-lens), and show how ala-lens can be organized into a symmetric monoidal category. We also show that sequential and parallel composition of well-behaved (wb) ala-lenses is also wb so that wb ala-lenses constitute a full subcategory of ala-lenses. 4 Formally, schema S A is a graph consisting of three arrows named Name, Expr., Depart., having the common source named OID and the targets String String String, Integer Integer Integer, String String String resp. This graph freely generates a category (just add four identity arrows) that we denote by S A again. We assume that a general model of such a schema is a functor X: S A → Rel Rel Rel that maps arrows to relations. If we need some of these relations to be functions, we label the arrows in the schema with a special constraint symbol, say, [fun], so that schema becomes a generalized sketch in the sense of Makkai (see [10,11]). In S A , all three arrows are labelled by [fun] so that a legal model must map them to functions. For example, model A in the figure is given by functor _ A : S A → Rel Rel Rel with the following values: OID A = {#A, #J, #M }, sets String String String A and Integer Integer Integer A actually do not depend on A-they are the predefined sets of strings and integers resp., and Name A (#A) = Ann, Name A (#J) = John, Expr. A (#A) = 10, etc.

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“…VICToRy is designed for -but not limited to -being connected to a TGG component of eMoflon. Currently, both eMoflon::IBeX [31] and eMoflon::Neo 5 implement the interface to the debugger. eMoflon::IBeX uses the incremental pattern matcher Democles [30] for matching patterns to graph instances which are loaded from XML metadata interchange (XMI) files into main memory, whereas eMoflon::Neo uses the external graph database Neo4J to collect matches and store models.…”

Section: Architecturementioning

confidence: 99%

“…Furthermore, a substantial number of tools have emerged that implement bidirectional model transformations based on TGGs. While in Fujaba [12] and the TGG Interpreter [13], operational rules are directly interpreted, MoTE [14,11], eMoflon [19,31], HenshinTGG [9] and EMorF [16] compile rules into source code of a GPL to be executed at runtime. A concept for debugging TGGs at different levels was introduced to the TGG Interpreter by Rieke [25].…”

Section: Related Workmentioning

confidence: 99%

VICToRy: Visual Interactive Consistency Management in Tolerant Rule-based Systems

Weidmann

Anjorin

Cheney

2020

Electron. Proc. Theor. Comput. Sci.

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In the field of Model-Driven Engineering, there exist numerous tools that support various consistency management operations including model transformation, synchronisation and consistency checking. The supported operations, however, typically run completely in the background with only input and output made visible to the user. We argue that this often reduces both understandability and controllability. As a step towards improving this situation, we present VICToRy, a debugger for model generation and transformation based on Triple Graph Grammars, a well-known rule-based approach to bidirectional transformation. In addition to a fine-grained, step-by-step, interactive visualisation, VICToRy enables the user to actively explore and choose between multiple valid rule applications thus improving control and understanding.

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Incremental (Unidirectional) Model Transformation with eMoflon::IBeX

Cited by 5 publications

References 12 publications

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

General Supervised Learning as Change Propagation with Delta Lenses

VICToRy: Visual Interactive Consistency Management in Tolerant Rule-based Systems

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