  The assurance of real-time properties is prone to context variability.
Providing such assurance at design time would require to check all the possible
context and system variations or to predict which one will be actually used.
Both cases are not viable in practice since there are too many possibilities to
foresee. Moreover, the knowledge required to fully provide the assurance for
self-adaptive systems is only available at runtime and therefore difficult to
predict at early development stages. Despite all the efforts on assurances for
self-adaptive systems at design or runtime, there is still a gap on verifying
and validating real-time constraints accounting for context variability. To
fill this gap, we propose a method to provide assurance of self-adaptive
systems, at design- and runtime, with special focus on real-time constraints.
We combine off-line requirements elicitation and model checking with on-line
data collection and data mining to guarantee the system's goals, both
functional and non-functional, with fine tuning of the adaptation policies
towards the optimization of quality attributes. We experimentally evaluate our
method on a simulated prototype of a Body Sensor Network system (BSN)
implemented in OpenDaVINCI. The results of the validation are promising and
show that our method is effective in providing evidence that support the
provision of assurance.
