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Template-based AADL automatic code generation

Authors:

Lihong ShangAuthors Info & Claims

Frontiers of Computer Science: Selected Publications from Chinese Universities, Volume 13, Issue 4

Pages 698 - 714

https://doi.org/10.1007/s11704-017-6477-y

Published: 01 August 2019 Publication History

Abstract

Embedded real-time systems employ a variety of operating system platforms. Consequently, for automatic code generation, considerable redevelopment is needed when the platform changes. This results in major challenges with respect to the automatic code generation process of the architecture analysis and design language (AADL). In this paper, we propose a method of template-based automatic code generation to address this issue. Templates are used as carriers of automatic code generation rules from AADL to the object platform. These templates can be easily modified for different platforms. Automatic code generation for different platforms can be accomplished by formulating the corresponding generation rules and transformation templates. We design a set of code generation templates from AADL to the object platform and develop an automatic code generation tool. Finally, we take a typical Data Processing Unit (DPU) system as a case study to test the tool. It is demonstrated that the auto-generated codes can be compiled and executed successfully on the object platform.

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Cited By

Li CChang JWang XZhao LMao W(2022)Formalization of Natural Language into PPTL Specification via Neural Machine TranslationStructured Object-Oriented Formal Language and Method10.1007/978-3-031-29476-1_7(79-92)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-29476-1_7

Template-based AADL automatic code generation
1. Applied computing
  1. Enterprise computing

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cover image Frontiers of Computer Science: Selected Publications from Chinese Universities

Frontiers of Computer Science: Selected Publications from Chinese Universities Volume 13, Issue 4

August 2019

241 pages

ISSN:2095-2228

EISSN:2095-2236

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Copyright © Copyright © 2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 August 2019

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Cited By

Li CChang JWang XZhao LMao W(2022)Formalization of Natural Language into PPTL Specification via Neural Machine TranslationStructured Object-Oriented Formal Language and Method10.1007/978-3-031-29476-1_7(79-92)Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-29476-1_7

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