  The increasing use of machine-learning (ML) enabled systems in critical tasks
fuels the quest for novel verification and validation techniques yet grounded
in accepted system assurance principles. In traditional system development,
model-based techniques have been widely adopted, where the central premise is
that abstract models of the required system provide a sound basis for judging
its implementation. We posit an analogous approach for ML systems using an ML
technique that extracts from the high-dimensional training data implicitly
describing the required system, a low-dimensional underlying structure--a
manifold. It is then harnessed for a range of quality assurance tasks such as
test adequacy measurement, test input generation, and runtime monitoring of the
target ML system. The approach is built on variational autoencoder, an
unsupervised method for learning a pair of mutually near-inverse functions
between a given high-dimensional dataset and a low-dimensional representation.
Preliminary experiments establish that the proposed manifold-based approach,
for test adequacy drives diversity in test data, for test generation yields
fault-revealing yet realistic test cases, and for runtime monitoring provides
an independent means to assess trustability of the target system's output.
