  In real-time systems, priorities assigned to real-time tasks determine the
order of task executions, by relying on an underlying task scheduling policy.
Assigning optimal priority values to tasks is critical to allow the tasks to
complete their executions while maximizing safety margins from their specified
deadlines. This enables real-time systems to tolerate unexpected overheads in
task executions and still meet their deadlines. In practice, priority
assignments result from an interactive process between the development and
testing teams. In this article, we propose an automated method that aims to
identify the best possible priority assignments in real-time systems,
accounting for multiple objectives regarding safety margins and engineering
constraints. Our approach is based on a multi-objective, competitive
coevolutionary algorithm mimicking the interactive priority assignment process
between the development and testing teams. We evaluate our approach by applying
it to six industrial systems from different domains and several synthetic
systems. The results indicate that our approach significantly outperforms both
our baselines, i.e., random search and sequential search, and solutions defined
by practitioners.Our approach scales to complex industrial systems as an
offline analysis method that attempts to find near-optimal solutions within
acceptable time, i.e., less than 16 hours.
