A conservative theory for long term reliability growth prediction

Bishop, Peter; Bloomfield, Robin

doi:10.1109/issre.1996.558887

Cited by 25 publications

(33 citation statements)

References 15 publications

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“…Variance is often used as a measure of distribution "broadness"; but lower variance does not guarantee lower reliability (except in the extreme case of zero variance): we can produce two distributions with the same mean and variance, and yet leading to different reliability functions. 6 However, we now show that there is a specific, nonparametric meaning of "one distribution being broader than another" that always implies greater reliability, irrespective of membership or not of parametric distribution families, provided the two distributions are comparable in this special sense. Indeed, we observe that changing the probability density function for a pfd by shifting probability masses apart from each other, towards opposite extremes of the range 7 , while keeping the mean pfd constant ( Figure 5), creates a new probability density function that: (i) is "broader" in the common sense of the word, and (ii) also implies higher reliability, for any t>1.…”

Section: Resumption Of Operation After Failure: Predicting the Numbermentioning

confidence: 65%

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Bounds on survival probability given mean probability of failure per demand; and the paradoxical advantages of uncertainty

Strigini

Wright

2014

Reliability Engineering & System Safety

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When deciding whether to accept into service a new safety-critical system, or choosing between alternative systems, uncertainty about the parameters that affect future failure probability may be a major problem. This uncertainty can be extreme if there is the possibility of unknown design errors (e.g. in software), or wide variation between nominally equivalent components.We study the effect of parameter uncertainty on future reliability (survival probability), for systems required to have low risk of even only one failure or accident over the long term (e.g. their whole operational lifetime) and characterised by a single reliability parameter (e.g. probability of failure per demand -pfd ). A complete mathematical treatment requires stating a probability distribution for any parameter with uncertain value. This is hard, so calculations are often performed using point estimates, like the expected value.We investigate conditions under which such simplified descriptions yield reliability values that are sure to be pessimistic (or optimistic) bounds for a prediction based on the true distribution. Two important observations are: (i) using the expected value of the reliability parameter as its true value guarantees a pessimistic estimate of reliability, a useful property in most safety-related decisions; (ii) with a given expected pfd, broader distributions (in a formally defined meaning of "broader"), that is, systems that are a priori "less predictable", lower the risk of failures or accidents.Result (i) justifies the simplification of using a mean in reliability modelling; we discuss within which scope this justification applies, and explore related scenarios, e.g. how things improve if we can test the system before operation. Result (ii) offers more flexible ways of bounding reliability predictions, but also has important, often counter-intuitive implications for decision making in various areas, like selection of components, project management, and product acceptance or licensing. For instance, in regulatory decision making dilemmas may arise in which the goal of minimising risk runs counter to other commonly held priorities, like predictability of risk; in safety assessment using expert opinion, the commonly recognised risk of experts being "overconfident" may be less dangerous than their being under confident. 1 CSR TECHNICAL REPORT Contents IntroductionPredictions of reliability and safety through probabilistic modelling depend on the values of model parameters, e.g. component failure rates, which are often uncertain. The main application scenario that motivates our research involves decisions on accepting a software product for use in a safety critical application requiring low accident probability over the operational life of the system in which it is embedded. For instance, an explicit requirement in civil aviation is that "catastrophic failure conditions" be "so unlikely that they are not anticipated to occur during the entire operational life of all airplanes of one type" [16]. Nuclear power pro...

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Section: Resumption Of Operation After Failure: Predicting the Numbermentioning

confidence: 65%

“…Then, the kind of reliability growth to be expected would depend not only on the pfd of S, but on how many faults contribute to it, and the amounts of their individual contribution. A discussion under simplifying assumptions is in [6].…”

Section: Effects Of Acceptance Testing On Improving Systemsmentioning

confidence: 99%

Bounds on survival probability given mean probability of failure per demand; and the paradoxical advantages of uncertainty

Strigini

Wright

2014

Reliability Engineering & System Safety

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“…As t grows, (t) grows with logarithmic increase. It should be noted that this model has a relationship to the well-known Logarithmic Poisson SRGM and the failure rate bound proposed Bishop and Bloomfield [5]. They have also proposed an effort-based model (AMEB); however, it is not comparable to the other models examined here.…”

Section: Anderson Thermodynamic Model (At)mentioning

confidence: 78%

Modeling the Vulnerability Discovery Process

Alhazmi

Malaiya

16th IEEE International Symposium on Software Reliability Engineering (ISSRE'05)

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Security vulnerabilities in servers and operating systems are software defects that represent great risks.Both software developers and users are struggling to contain the risk posed by these vulnerabilities. The vulnerabilities are discovered by both developers and external testers throughout the life-span of a software system. A few models for the vulnerability discovery process have just been published recently.Such models will allow effective resource allocation for patch development and are also needed for evaluating the risk of vulnerability exploitation. Here we examine these models for the vulnerability discovery process. The models are examined both analytically and using actual data on vulnerabilities discovered in three widely-used systems. The applicability of the proposed models and significance of the parameters involved are discussed. The limitations of the proposed models are examined and major research challenges are identified.

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“…There is evidence of a fairly strong correlation for systems such as programmable logic controllers (PLCs) [13]. This correlation can be used produce a stochastic model of functional failure.…”

Section: Predicting Failure Ratesmentioning

confidence: 99%