software intensive
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2021 ◽  
pp. 111152
Author(s):  
Simon Butler ◽  
Jonas Gamalielsson ◽  
Björn Lundell ◽  
Christoffer Brax ◽  
Anders Mattsson ◽  
...  

2021 ◽  
Author(s):  
Lea Gerling ◽  
Sandra Greiner ◽  
Kristof Meixner ◽  
Gabriela Karoline Michelon

2021 ◽  
Author(s):  
Yanxiang Tong ◽  
Yi Qin ◽  
Yanyan Jiang ◽  
Chang Xu ◽  
Chun Cao ◽  
...  

Author(s):  
Antonio Gutierrez ◽  
Lisa Sonnleithner ◽  
Alois Zoitl ◽  
Rick Rabiser
Keyword(s):  

ZusammenfassungSoftware-intensive Cyber-Physische Produktionssysteme (SiCPPS) sind hochkomplexe vernetzte Systeme, die autonom miteinander und mit ihrer Umgebung interagieren, um flexibel eine Vielzahl an Produkten zu produzieren. Solche Systeme haben typischerweise eine lange Lebensdauer, über die eine Vielzahl von Varianten entwickelt und gewartet werden muss. Diese Systeme bestehen aus vielen heterogenen Hard- und Softwarekomponenten, welche auf verschiedene Arten konfiguriert und kombiniert werden können. Variabilität betrifft dabei nicht nur diese Komponenten, sondern auch Entwicklungsprozesse, Anwendungsdomänen sowie Methoden und Werkzeuge. In der Industrie hängt der Umgang mit Variabilität jedoch aktuell stark von Expertenwissen als auch von selbst entwickelten Werkzeugen ab. Diese funktionieren oft nur mit ganz bestimmten Artefakten bzw. Hard- und Softwareplattformen. Existierende Forschungsansätze aus dem Gebiet der SiCPPS beschäftigen sich nicht explizit mit der Beherrschung dieser Variabilität. In diesem Beitrag definieren wir eine Forschungsagenda zur Beherrschung der Variabilität in SiCPPS. Wir konzentrieren uns dabei auf die Entwicklung von Techniken (i) zur Erhebung und Modellierung von Variabilität und (ii) zur Automatisierung sowohl der Generierung von Artefakten in SiCPPS als auch der Generierung und Wartung von Modellen auf Basis bestehender Artefakte.


2021 ◽  
Vol 20 (5) ◽  
pp. 1-39
Author(s):  
Deniz Akdur

Many practitioners in the software-intensive embedded industry often face difficulties after beginning their careers due to misalignment of the skills learned at the university with what is required in the workplace. Companies spend crucial resources to train personnel whose academic backgrounds are not only based on “computing disciplines” but also on non-computing ones. Analyzing the gap between the software industry and academia is important for three reasons: (1) for employers, hiring properly trained practitioners allows them to spend less time in training them while incorporating them more efficiently into the workforce; (2) for practitioners, knowing the most important skillset is helpful to increase their chance of employability; and (3) for academia, understanding the necessary skillset is critical to making curriculum changes. To achieve these objectives, we conducted a survey that yielded responses from 659 software professionals working worldwide in different roles. In this study, we only included the responses of 393 embedded software practitioners whose undergraduate degree was completed in Turkey, working in 10 countries. This article sheds light on the most important skills in the embedded software industry by presenting various cross-factor analyses. Understanding the coverage of these skills in the curriculum (mostly in Turkish universities) helps bridge the gaps, which can and should be achieved through more Industry Academia Collaborations (IACs).


2021 ◽  
pp. 102705
Author(s):  
Khalil Drira ◽  
Carlos E. Cuesta

2021 ◽  
Vol 176 ◽  
pp. 110925
Author(s):  
Roberto Verdecchia ◽  
Philippe Kruchten ◽  
Patricia Lago ◽  
Ivano Malavolta

2021 ◽  
Author(s):  
Thomas Preußer ◽  
Smitha Gautham ◽  
Abhi Rajagopala ◽  
Carl Elks ◽  
Alexander Weiss

Decades of advances in computer architecture, software-intensive applications and system integration have created significant challenges for embedded systems designers and test engineers. Intrusive software instrumentation and breakpoint-based debugging are often viewed as the primary options for observing operational system internals. This narrow sight creates complicated test flows and convoluted debugging procedures. Modern embedded computing systems offer Embedded Trace as the technological answer to the encountered observability conundrum. Although an integral part of virtually all modern processors, it is frequently overlooked. Its technical foundations are little known to application engineers, test engineers, and project managers. This article explains Embedded Trace as an essential technology in the testing and debugging toolbox. It highlights its capabilities, limitations and opportunities.


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