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Learning-based Response Time Analysis in Real-Time Embedded Systems: A Simulation-based Approach
RISE - Research Institutes of Sweden, ICT, SICS.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-1512-0844
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-7879-4371
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0003-1597-6738
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Response time analysis is an essential task to verify the behavior of real-time systems. Several response time analysis methods have been proposed to address this challenge, particularly for real-time systems with different levels of complexity. Static analysis is a popular approach in this context, but its practical applicability is limited due to the high complexity of the industrial real-time systems, as well as many unpredictable run-time events in these systems. In this work-in-progress paper, we propose a simulation-based response time analysis approach using reinforcement learning to find the execution scenarios leading to the worst-case response time. The approach learns how to provide a practical estimation of the worst-case response time through simulating the program without performing static analysis. Our initial study suggests that the proposed approach could be applicable in the simulation environments of the industrial real-time control systems to provide a practical estimation of the execution scenarios leading to the worst-case response time.

Place, publisher, year, edition, pages
2018. p. 21-24
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:ri:diva-34196DOI: 10.1145/3194095.3194097Scopus ID: 2-s2.0-85051494988OAI: oai:DiVA.org:ri-34196DiVA, id: diva2:1232995
Conference
1st International Workshop on Software Qualities and their Dependencies
Available from: 2018-07-13 Created: 2018-07-13 Last updated: 2019-01-09Bibliographically approved

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Publisher's full textScopushttp://delivery.acm.org/10.1145/3200000/3194097/p21-moghadam.pdf?ip=194.218.146.225&id=3194097&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1532949375_eb884346f0d1232f46f7627efd09197d

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Saadatmand, MehrdadBorg, MarkusBohlin, Markus

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