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• W144739785 abstract "Triple graph grammars (TGGs) provide a formal framework for bidirectional model transformations. As in practice, TGGs are primarily used in pure model-to-model transformation scenarios, tools for text-to-model and model-to-text transformations make them also applicable in text-to-text transformation contexts. This paper presents a solution for the text-to-text transformation case study of the Transformation Tool Contest 2014 on translating FIXML (an XML notation for financial transactions) to source code written in Java, C# or C++. The solution uses the HenshinTGG tool for specifying and executing model-to-model transformations based on the formal concept of TGGs as well as the Xtext tool for parsing XML content to yield its abstract syntax tree (text-to-model transformation) and serialising abstract syntax trees to source code (model-to-text transformation). The approach is evaluated concerning a given set of criteria. Triple graph grammars (TGGs) provide a formal framework for specifying consistent integrated models of source and target models in bidirectional model transformations. Correspondences between the elements of source and target models are defined by triple rules, from which operational rules for forward and backward transformations are derived automatically [5, 9]. Several tool implementations for TGGs exist [7]. Numerous case studies have proven the applicability of TGGs in model-to-model (M2M) transformation scenarios [4, 3]. In [6], we presented an approach for applying TGGs in a text-to-text (T2T) transformation context for translating satellite procedures. We adapt this approach to provide a solution for the T2T transformation case study of the TTC 2014 [8]. We evaluate the solution based on fixed criteria: complexity, accuracy, development effort, fault tolerance, execution time, and modularity. As depicted in Fig. 1, our transformation involves these steps: A text-to-model (T2M) transformation step parses the the content of a FIXML file and yields its abstract syntax tree (AST). Then, a M2M transformation is performed based on a given TGG to convert the source AST into the target AST. Finally, the target AST is serialised back to source code via a model-to-text (M2T) transformation. We combine Xtext [1] with the HenshinTGG tool to perform the T2M and M2T steps via Xtext and the M2M step via HenshinTGG. Xtext is a tool for specifying domain specific textual languages and generating parsers and serialisers for them. The parser checks that the input source code is well-formed and the serialiser ensures that the generated output source code is well-defined. HenshinTGG is an extension of the EMF-Henshin tool [2] and is used for specifying and executing M2M transformations based on the formal concept of TGGs. The solution is available on SHARE 1 . The paper is structured as follows. Sec. 2 describes the TGG tool implementation HenshinTGG, Sec. 3 presents the details of our solution for the case study, Sec. 4 evaluates the solution concerning the given criteria and Sec. 5 provides a conclusion and describes potential extensions." @default.
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• W144739785 date "2014-01-01" @default.
• W144739785 modified "2023-09-26" @default.
• W144739785 title "Solving the FIXML2Code-case study with HenshinTGG" @default.
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