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Sarah Brocklehurst

dblp:00/4294 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1993
0000-0002-1636-1017ORCID · corroborated

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

Security and privacy · 1Software 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
Software testing · 100%

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

TopicWeightPapersLastEvidence papers
Software testing
software reliability
0.011990
Recalibrating Software Reliability Models · IEEE Trans. Software Eng. 1990
Software testing › software reliability › software reliability modeling
software reliability growth model
0.011990
Recalibrating Software Reliability Models · IEEE Trans. Software Eng. 1990

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

u-plot · 0.0simulation · 0.0
YearPublicationVenuePosition
1993 Towards Operational Measures of Computer Security
abstract
Ideally, a measure of the security of a system should capture quantitatively the intuitive notion of ‘the ability of the system to resist attack’. That is, it should be operational, reflecting the degree to which the system can be expected to remain
Bev Littlewood, Sarah Brocklehurst, Norman E. Fenton, Peter Mellor, Stella Page, David Wright 0001, John Dobson, John A. McDermid, Dieter Gollmann
J. Comput. Secur.2
1990 Recalibrating Software Reliability Models
abstract
There is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. A technique of analyzing predictive accuracy called the u-plot allows a user to estimate the relationship between the predicted reliability and the true reliability. It is shown how this can be used to improve reliability predictions in a very general way by a process of recalibration. Simulation results show that the technique gives improved reliability predictions in a large proportion of cases. However, a user does not need to trust the efficacy of recalibration, since the new reliability estimates produced by the technique are truly predictive and their accuracy in a particular application can be judged using the earlier methods. The generality of this approach suggests its use whenever a software reliability model is used. Indeed, although this work arose from the need to address the poor performance of software reliability models, it is likely to have applicability in other areas such as reliability growth modeling for hardware.>
Sarah Brocklehurst, P. Y. Chan 0002, Bev Littlewood, John Snell
IEEE Trans. Software Eng.1