  Autonomous traffic control systems are large-scale systems with critical
goals. Due to the dynamic nature of the surrounding world of these systems,
assuring the satisfaction of their properties at runtime and in the presence of
a change is important. A prominent approach to assure the correct behavior of
these systems is verification at runtime, which has strict time and memory
limitations. To tackle these limitations, we propose Magnifier, an iterative,
incremental, and compositional verification approach that operates on a
component-based model. The Magnifier idea is zooming on the component affected
by a change, verifying the correctness of properties of interest of the system
after adapting the component to the change, and then zooming out and tracing
the change if it propagates. If the change propagates, all components affected
by the change are adapted and are composed to form a new component. Magnifier
repeats the same process for the new component. This iterative process
terminates whenever the propagation of the change stops. In Magnifier, we use
the Coordinated Adaptive Actor model (CoodAA) of traffic control systems. We
present a formal semantics for CoodAA as a network of Timed Input-Output
Automata (TIOAs). The change does not propagate if TIOAs of the adapted
component and its environment are compatible. We implement our approach in
Ptolemy II. The results of our experiments indicate that the proposed approach
improves the verification time and the memory consumption compared to a
non-compositional approach.
