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In recent work we have argued for a formal treatment of confidence about the claims made in dependability cases for software-based systems. The key idea underlying this work is 'the inevitability of uncertainty': it is rarely possible to assert that a claim about safety or reliability is true with certainty. Much of this uncertainty is epistemic in nature, so it seems inevitable that expert judgment will continue to play an important role in dependability cases. Here we consider a simple case where an expert makes a claim about the probability of failure on demand (pfd) of a sub-system of a wider system, and is able to express his confidence about that claim probabilistically. An important, but difficult, problem then is how such sub-system (claim, confidence) pairs can be propagated through a dependability case for a wider system, of which the sub-systems are components. An informal way forward is to justify, at high confidence, a strong claim, and then conservatively only claim something much weaker: "I'm 99% confident that the pfd is less than 10 -5 , so it's reasonable to be 100% confident that it is less than 10 -3 ." These conservative pfds of sub-systems can then be propagated simply through the dependability case of the wider system. In this paper we provide formal support for such reasoning.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-This paper provides experimental and theoretical evidence for the existence of contiguous failure regions in the program input space ('blob' defects). For real-time systems where successive input values tend to be similar, blob defects can have a major impact on the software survival time because the failure probability is not constant. For example, with a 'random walk' input sequence, the probability of failure decreases as the time from the last failure increases. It is shown that the key factors affecting the survival time are the input 'trajectory', the rate of change of the input values and the 'surface area' of the defect (rather than its volume). Permanent

We have previously reported [1] the results of an exploratory analysis of the potential gains in detection capability from using diverse AntiVirus products. The analysis was based on 1599 malware samples collected from a distributed honeypot deployment over a period of 178 days. The malware samples were sent to the signature engines of 32 different AntiVirus products hosted by the VirusTotal service. The analysis suggested significant gains in detection capability from using more than one AntiVirus product in a one-out-oftwo intrusion-tolerant setup. In this paper we present new analysis of this dataset to explore the detection gains that can be achieved from using more diversity (i.e. more than two AntiVirus products), how diversity may help to reduce the "at risk time" of a system and a preliminary model-fitting using the hyper-exponential distribution.