  Cyber-physical systems (CPSs) typically consist of a wide set of integrated,
heterogeneous components; consequently, most of their critical failures relate
to the interoperability of such components.Unfortunately, most CPS test
automation techniques are preliminary and industry still heavily relies on
manual testing. With potentially incomplete, manually-generated test suites, it
is of paramount importance to assess their quality. Though mutation analysis
has demonstrated to be an effective means to assess test suite quality in some
specific contexts, we lack approaches for CPSs. Indeed, existing approaches do
not target interoperability problems and cannot be executed in the presence of
black-box or simulated components, a typical situation with CPSs.
  In this paper, we introduce data-driven mutation analysis, an approach that
consists in assessing test suite quality by verifying if it detects
interoperability faults simulated by mutating the data exchanged by software
components. To this end, we describe a data-driven mutation analysis technique
(DaMAT) that automatically alters the data exchanged through data buffers. Our
technique is driven by fault models in tabular form where engineers specify how
to mutate data items by selecting and configuring a set of mutation operators.
  We have evaluated DaMAT with CPSs in the space domain; specifically, the test
suites for the software systems of a microsatellite and nanosatellites launched
on orbit last year. Our results show that the approach effectively detects test
suite shortcomings, is not affected by equivalent and redundant mutants, and
entails acceptable costs.
