Double Depth First Search Based Parametric Analysis for Parametric Time-Interval Automata

Abstract

In this paper, we propose a parametric model checking algorithm for a subclass of Timed Automata called Parametric Time-Interval Automata (PTIA). In a PTIA, we can specify upper- and lower-bounds of the execution time (time-interval) of each transition using parameter variables. The proposed algorithm takes two inputs, a model described in a PTIA and a property described in a PTIA accepting all invalid infinite/finite runs (called a never claim), or valid finite runs of the model. In the proposed algorithm, firstly we determinize and complement the given property PTIA if it accepts valid finite runs. Secondly, we accelerate the given model, that is, we regard all the actions that are not appeared in the given property PTIA as invisible actions and eliminate them from the model while preserving the set of visible traces and their timings. Thirdly, we construct a parallel composition of the model and the property PTIAs which is accepting all invalid runs that are accepted by the model. Finally, we perform the extension of Double Depth First Search (DDFS), which is used in the automata-theoretic approach to Linear-time Temporal Logic (LTL) model checking, to derive the weakest parameter condition in order that the given model never executes the invalid runs specified by the given property.