Tesi etd-10292020-180740 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
PALADINO, FRANCESCO
URN
etd-10292020-180740
Titolo
A code generator for state machine models in automotive applications
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
EMBEDDED COMPUTING SYSTEMS
Relatori
relatore Prof. Di Natale, Marco
correlatore Dott. Gai, Paolo
correlatore Dott. Gai, Paolo
Parole chiave
- automotive
- code generation
- state machine
Data inizio appello
20/11/2020
Consultabilità
Non consultabile
Data di rilascio
20/11/2090
Riassunto
Model-based development of automotive embedded systems allows to improve the productivity and quality of applications by allowing a higher level of abstraction for control engineers, early verification and validation by simulation or model checking and the automatic generation of a code implementation.
Extended finite state machines are a fundamental component of every modelling language and tool, since they allow to describe behaviour that depends on states or operational modes.
The objective of this thesis is to implement a code generator for state machine models to be used in automotive applications, that is complying with the AUTOSAR standard when applicable. The framework developed in this thesis is built on the Eclipse Modeling Framework and leverages the open source Acceleo model-to-text transformation engine. The input models are obtained from the open source Ptolemy modeller from the University of California at Berkeley.
The code generator is capable of generating production-level code with a performance comparable to that of commercial generators.
Extended finite state machines are a fundamental component of every modelling language and tool, since they allow to describe behaviour that depends on states or operational modes.
The objective of this thesis is to implement a code generator for state machine models to be used in automotive applications, that is complying with the AUTOSAR standard when applicable. The framework developed in this thesis is built on the Eclipse Modeling Framework and leverages the open source Acceleo model-to-text transformation engine. The input models are obtained from the open source Ptolemy modeller from the University of California at Berkeley.
The code generator is capable of generating production-level code with a performance comparable to that of commercial generators.
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La tesi non è consultabile. |