  Traditionally, fault- or event-tree analyses or FMEAs have been used to
estimate the probability of a safety-critical device creating a dangerous
condition. However, these analysis techniques are less effective for systems
primarily reliant on software, and are perhaps least effective in Safety of the
Intended Functionality (SOTIF) environments, where the failure or dangerous
situation occurs even though all components behaved as designed. This paper
describes an approach we are considering at BlackBerry QNX: using Bayesian
Belief Networks to predict defects in embedded software, and reports on early
results from our research.
