Symbolic assume-guarantee reasoning through BDD learning

Abstract

Both symbolic model checking and assume-guarantee reasoning aim to circumvent the state explosion problem. Symbolic model checking explores many states simultaneously and reports numerous erroneous traces. Automated assume-guarantee reasoning, on the other hand, infers contextual assumptions by inspecting spurious erroneous traces. One would expect that their integration could further improve the capacity of model checking. Yet examining numerous erroneous traces to deduce contextual assumptions can be very time-consuming. The integration of symbolic model checking and assume-guarantee reasoning is thus far from clear. In this paper, we present a progressive witness analysis algorithm for automated assume-guarantee reasoning to exploit a multitude of traces from BDD-based symbolic model checkers. Our technique successfully integrates symbolic model checking with automated assume-guarantee reasoning by directly inferring BDD's as implicit assumptions. It outperforms monolithic symbolic model checking in four benchmark problems and an industrial case study in experiments.