  Estimating worst-case execution times (WCET) is an important activity at
early design stages of real-time systems. Based on WCET estimates, engineers
make design and implementation decisions to ensure that task executions always
complete before their specified deadlines. However, in practice, engineers
often cannot provide precise point WCET estimates and prefer to provide
plausible WCET ranges. Given a set of real-time tasks with such ranges, we
provide an automated technique to determine for what WCET values the system is
likely to meet its deadlines, and hence operate safely with a probabilistic
guarantee. Our approach combines a search algorithm for generating worst-case
scheduling scenarios with polynomial logistic regression for inferring
probabilistic safe WCET ranges. We evaluated our approach by applying it to
three industrial systems from different domains and several synthetic systems.
Our approach efficiently and accurately estimates probabilistic safe WCET
ranges within which deadlines are likely to be satisfied with a high degree of
confidence.
