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Janet R. Dunham

dblp:12/5993 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1986
—ORCID · none

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

Software engineering, systems software and programming languages · 1 · 1 first-author

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.

Software engineering, system software, and programming languages
1 paper
Operating systems · 56% Software testing · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Operating systems
fault tolerance
0.011986
Experiments in Software Reliability: Life-Critical Applications · IEEE Trans. Software Eng. 1986
Software testing
software reliability
0.011986
Experiments in Software Reliability: Life-Critical Applications · IEEE Trans. Software Eng. 1986
Operating systems › fault tolerance
n-version programming
0.011986
Experiments in Software Reliability: Life-Critical Applications · IEEE Trans. Software Eng. 1986

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

repetitive run modeling · 0.0reliability data gathering experiments · 0.0
YearPublicationVenuePosition
1986 Experiments in Software Reliability: Life-Critical Applications
abstract
Digital computers are being used more frequently for process control applications in which the cost of system failure is high. Consideration of the potentially life-threatening risk, resulting from the high degree of functionality being ascribed to the software components of these systems, has stimulated the recommendation of various designs for tolerating software faults. The author discusses four reliability data gathering experiments which were conducted using a small sample of programs for two problems having ultrareliability requirements:n-version programming for fault detection, and repetitive run modeling for failure and fault rate estimation. The experimental results agree with those of M. Nagel and J.A. Skrivan (1982) in that the program error rates suggest an approximate log-linear pattern and the individual faults occurred with significantly different error rates.
Janet R. Dunham
IEEE Trans. Software Eng.1