Predicting Stack Use in Embedded Software Applications
Main Author: | |
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Publication Date: | 2019 |
Format: | Master thesis |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | https://hdl.handle.net/10216/119493 |
Summary: | On mass market products the production cost is one of the main concerns. This constraint also applies to electronic controller units (ECU) used in the automotive industry, leading to a choice of computerize systems with limited resources (such as Code Flash, Data Flash, RAM, Stack usage and CPU load). It therefore becomes essential to monitor the resources used by the embedded software during its development. The monitoring ensures that the architecture, the implementation and functionality all fit within the hardware limitations. The resource monitoring may be done at compile time using static analyses techniques, or during runtime. The former predicts the use of resources by analyzing the source code. The latter focuses on analysis at runtime. This dissertation describes the architecture and implementation of a tool to monitor the use of the stack resource by all the embedded software an ECU. A prediction for stack use is obtained during the software development process using a static analysis approach. With the help of the tool, embedded software developers can determine an upper bound for the use of Stack with a higher level of trust. |
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Predicting Stack Use in Embedded Software ApplicationsEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringOn mass market products the production cost is one of the main concerns. This constraint also applies to electronic controller units (ECU) used in the automotive industry, leading to a choice of computerize systems with limited resources (such as Code Flash, Data Flash, RAM, Stack usage and CPU load). It therefore becomes essential to monitor the resources used by the embedded software during its development. The monitoring ensures that the architecture, the implementation and functionality all fit within the hardware limitations. The resource monitoring may be done at compile time using static analyses techniques, or during runtime. The former predicts the use of resources by analyzing the source code. The latter focuses on analysis at runtime. This dissertation describes the architecture and implementation of a tool to monitor the use of the stack resource by all the embedded software an ECU. A prediction for stack use is obtained during the software development process using a static analysis approach. With the help of the tool, embedded software developers can determine an upper bound for the use of Stack with a higher level of trust.2019-02-062019-02-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/119493TID:202390403engCarlos Daniel Alves Garciainfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-27T19:52:54Zoai:repositorio-aberto.up.pt:10216/119493Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T23:36:34.611133Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Predicting Stack Use in Embedded Software Applications |
title |
Predicting Stack Use in Embedded Software Applications |
spellingShingle |
Predicting Stack Use in Embedded Software Applications Carlos Daniel Alves Garcia Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Predicting Stack Use in Embedded Software Applications |
title_full |
Predicting Stack Use in Embedded Software Applications |
title_fullStr |
Predicting Stack Use in Embedded Software Applications |
title_full_unstemmed |
Predicting Stack Use in Embedded Software Applications |
title_sort |
Predicting Stack Use in Embedded Software Applications |
author |
Carlos Daniel Alves Garcia |
author_facet |
Carlos Daniel Alves Garcia |
author_role |
author |
dc.contributor.author.fl_str_mv |
Carlos Daniel Alves Garcia |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
description |
On mass market products the production cost is one of the main concerns. This constraint also applies to electronic controller units (ECU) used in the automotive industry, leading to a choice of computerize systems with limited resources (such as Code Flash, Data Flash, RAM, Stack usage and CPU load). It therefore becomes essential to monitor the resources used by the embedded software during its development. The monitoring ensures that the architecture, the implementation and functionality all fit within the hardware limitations. The resource monitoring may be done at compile time using static analyses techniques, or during runtime. The former predicts the use of resources by analyzing the source code. The latter focuses on analysis at runtime. This dissertation describes the architecture and implementation of a tool to monitor the use of the stack resource by all the embedded software an ECU. A prediction for stack use is obtained during the software development process using a static analysis approach. With the help of the tool, embedded software developers can determine an upper bound for the use of Stack with a higher level of trust. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-02-06 2019-02-06T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/119493 TID:202390403 |
url |
https://hdl.handle.net/10216/119493 |
identifier_str_mv |
TID:202390403 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
collection |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
repository.name.fl_str_mv |
Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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