  Dependability assurance of systems embedding machine learning(ML)
components---so called learning-enabled systems (LESs)---is a key step for
their use in safety-critical applications. In emerging standardization and
guidance efforts, there is a growing consensus in the value of using assurance
cases for that purpose. This paper develops a quantitative notion of assurance
that an LES is dependable, as a core component of its assurance case, also
extending our prior work that applied to ML components. Specifically, we
characterize LES assurance in the form of assurance measures: a probabilistic
quantification of confidence that an LES possesses system-level properties
associated with functional capabilities and dependability attributes. We
illustrate the utility of assurance measures by application to a real world
autonomous aviation system, also describing their role both in i) guiding
high-level, runtime risk mitigation decisions and ii) as a core component of
the associated dynamic assurance case.
