ATTEST: Automated and Thorough Testing of Embedded Software in Teaching

Meinhard Kissich, Klaus Weinbauer*, Marcel Carsten Baunach*

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

Dependability requirements are getting increasingly stringent in embedded systems, demanding highly skilled developers. One crucial point in building up expertise is getting precise feedback in programming courses at university to recognize flaws and learn from mistakes. Depending on the assignment and learning outcome, the assessment may include testing for the implementation’s completeness, correctness, performance, and robustness. A timely and in-depth review for a large number of course participants relies on test automation. However, embedded software often includes hardware-dependent code that can only be executed on the target device. Thus, we provide an open-source and remote hardware-in-the-loop testing solution with pre-defined test cases for embedded software particularly designed for teaching in university courses. This paper defines and elaborates on the requirements, gives an insight into design decisions, and evaluates the test system on metrics of our Real-Time Operating Systems course.
Originalspracheenglisch
TitelProceedings of the 5th European Conference on Software Engineering Education, ECSEE 2023
Seiten199-203
Seitenumfang5
ISBN (elektronisch)978-1-4503-9956-2
DOIs
PublikationsstatusVeröffentlicht - 19 Juni 2023
Veranstaltung5th European Conference on Software Engineering Education: ECSEE 2023 - Seeon Monastery, Seeon, Deutschland
Dauer: 19 Juni 202321 Juni 2023
Konferenznummer: 5
http://ecsee.eu/home/

Publikationsreihe

NameACM International Conference Proceeding Series

Konferenz

Konferenz5th European Conference on Software Engineering Education
KurztitelECSEE
Land/GebietDeutschland
OrtSeeon
Zeitraum19/06/2321/06/23
Internetadresse

ASJC Scopus subject areas

  • Software
  • Human-computer interaction
  • Maschinelles Sehen und Mustererkennung
  • Computernetzwerke und -kommunikation

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