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Jens Nordahl

dblp:96/2006 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1993
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
dependability modeling
0.011993
From CSP Models to Markov Models · IEEE Trans. Software Eng. 1993
Performance modeling and evaluation
markov models
0.011993
From CSP Models to Markov Models · IEEE Trans. Software Eng. 1993

Methods — techniques the papers use, named apart from their topics

finite automaton transformation · 0.0communicating sequential processes · 0.0
YearPublicationVenuePosition
1993 Composition and Refinement of Probabilistic Real-Time Systems
Jens Nordahl, Erling Vagn Sørensen
SAFECOMP2
1993 From CSP Models to Markov Models
abstract
It is shown how a probabilistic dependability model of a safety-critical system can be derived from a trace-based functional model of the system. The functional model is a communicating sequential process (CSP) that includes command, failure, and repair events. The dependability model is a time homogeneous Markov process with transitions determined by these events. The method applies to deterministic systems that can be described in terms of a finite number of states and in which all event occurrences are stochastic with exponential time distribution. The derivation is carried out in two steps. An algorithmic determination is made of a finite automaton from the specification of the CSP process. The automaton is transformed into a Markov process. The Markov model for this system is used to determine the waiting time to terminal failure. The theory is applied to a larger and more realistic example: a gas burner system operating in the on-off mode. For this system, the waiting time to terminal failure is calculated, and the number of failures per year in a large population of identical, independently operated systems is estimated.>
Erling Vagn Sørensen, Jens Nordahl, Niels Herman Hansen
IEEE Trans. Software Eng.2