A feature model for model-to-text transformation languages

Rose, Martin; Matragkas,; Kolovos, Dimitrios S.; Paige,

doi:10.1109/mise.2012.6226015

Cited by 20 publications

(10 citation statements)

References 6 publications

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“…Rose et al [66] extended the feature model of Czarnecki et al to focus on template-based model-to-text transformation tools. Their classification is centered exclusively on tool-dependent features.…”

Section: Literature Reviews On Code Generationmentioning

confidence: 99%

Systematic mapping study of template-based code generation

Syriani

Luhunu

Sahraoui

2018

Computer Languages, Systems & Structures

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Template-based code generation (TBCG) is a synthesis technique that produces code from high-level specifications, called templates. TBCG is a popular technique in model-driven engineering (MDE) given that they both emphasize abstraction and automation. Given the diversity of tools and approaches, it is necessary to classify existing TBCG techniques to better guide developers in their choices. The goal of this article is to better understand the characteristics of TBCG techniques and associated tools, identify research trends, and assess the importance of the role of MDE in this code synthesis approach. We conducted a systematic mapping study of the literature to paint an interesting picture about the trends and uses of TBCG. Our study shows that the community has been diversely using TBCG over the past 15 years. TBCG has greatly benefited from MDE. It has favored a template style that is output-based and high level modeling languages as input. TBCG is mainly used to generate source code and has been applied in a variety of domains. Furthermore, both MDE and non-MDE tools are becoming effective development resources in industry.

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Section: Literature Reviews On Code Generationmentioning

confidence: 99%

Systematic mapping study of template-based code generation

Syriani

Luhunu

Sahraoui

2018

Computer Languages, Systems & Structures

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“…In practice, the parser is usually generated from the grammar by a parser generator, like ANTLR [24,25], Bison [26], or Yacc [27,28]. The inverse of the T2M transformation is the model-to-text (M2T) transformation [29,30]. During this process, we generate the textual notation from the model.…”

Section: Processes and Artifacts In Text-based Modelingmentioning

confidence: 99%

A Survey on Text-based Modeling in Model Evolution and Management

Somogyi¹,

Asztalos²

2019

Period. Polytech. Elec. Eng. Comp. Sci.

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Model-driven software engineering methodologies like model-driven engineering aim to improve the productivity of software development by using graph-based models as the main artifacts during development, and generating the source code from these models. The models are usually displayed and edited using a graphical notation. However, they can also be described using a textual notation. This has some advantages and disadvantages compared to the graphical approach. For example, while editing the model, we can better focus on the details instead of a broad overview. Similarly to source code, models evolve rapidly during development. Handling and managing the evolution of models is an important task in model-driven methodologies and is an active research area today. However, there exist few research on text-based modeling approaches, compared to graph-based ones. This paper introduces the text-based modeling research field based on existing literature, and presents the state-of-the-art of the field related to model evolution and management. Our goal is to identify challenges and directions for future research in this field. The main topics covered are model differencing and merging, and the synchronization of the textual and graphical notations.

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“…The model transformation activity has been Berrani et al, 2013, Bouquet et al, 2012, Doligalski and Adamski, 2013, Mueller et al, 2010, Greenyer and Kindler, 2010, Ko et al, 2013, Gronmo et al, 2009, Feher and Lengyel, 2012, de Lara and Guerra, 2014, Kolahdouz-Rahimi et al, 2014, Rutle et al, 2012, Huang et al, 2012 Model to Text (M2T) 9 DeTommasi et al, 2013, Moreira et al, 2010, Linehan and Clarke, 2012, Herrera et al, 2014, Zohaib Iqbal et al, 2013, Rose et al, 2012, Ledo et al, 2012, Ouchani et al, 2014 M2M and M2T 12 Giuseppe Di et al, 2013, Vidal et al, 2009, Lecomte et al, 2011, Rafiq Quadri et al, 2012, Elhaji et al, 2012, Riccobene and Scandurra, 2012, Doering, 2014, Andrea Bollati et al, 2013, Baresi et al, 2013, Bousse et al, 2012 performed in overall 35 researches. We classified these researches into three transformation types i.e.…”

Section: Model Transformation Techniquesmentioning

confidence: 99%