Model checking suffers from the state space explosion problem. Compositional verification techniques such as assume-guarantee reasoning (AGR) have been proposed to alleviate the problem. However, there are at least three challenges in applying AGR. Firstly, given a system M1 ? M2, how do we automatically construct and refine (in the presence of spurious counterexamples) an assumption A2, which must be an abstraction of M2? Previous approaches suggest to incrementally learn and modify the assumption through multiple invocations of a model checker, which could be often time consuming. Secondly, how do we keep the state space small when checking M1 ? A2 = f if multiple refinements of A2 are necessary? Lastly, in the presence of multiple parallel components, how do we partition the components? In this work, we propose interpolation-guided compositional verification. The idea is to tackle three challenges by using interpolations to generate and refine the abstraction of M2, to abstract M1 at the same time (so that the state space is reduced even if A2 is refined all the way to M2), and to find good partitions. Experimental results show that the proposed approach outperforms existing approaches consistently.

Non-determinism in concurrent or distributed software systems (i.e., various possible execution orders among different distributed components) presents new challenges to the existing reliability analysis methods based on Markov chains. In this work, we present a toolkit RaPiD for the reliability analysis of non-deterministic systems. Taking Markov decision process as reliability model, RaPiD can help in the analysis of three fundamental and rewarding aspects regarding software reliability. First, to have reliability assurance on a system, RaPiD can synthesize the overall system reliability given the reliability values of system components. Second, given a requirement on the overall system reliability, RaPiD can distribute the reliability requirement to each component. Lastly, RaPiD can identify the component that affects the system reliability most significantly. RaPiD has been applied to analyze several real-world systems including a financial stock trading system, a proton therapy control system and an ambient assisted living room system.

BDD-based symbolic model checking is capable of verifying systems with a large number of states. In this work, we report an extensible framework to facilitate symbolic encoding and checking of hierarchical systems. Firstly, a novel library of symbolic encoding functions for compositional operators (e.g., parallel composition, sequential composition, choice operator, etc.) are developed so that users can apply symbolic model checking techniques to hierarchical systems with little knowledge of symbolic encoding techniques (like BDD or CUDD). Secondly, as the library is language-independent, we build an extensible framework with various symbolic model checking algorithms so that the library can be easily applied to encode and verify different modeling languages. Lastly, the applicability and scalability of our framework are demonstrated by applying the framework in the development of symbolic model checkers for three modeling languages as well as a comparison with the NuSMV model checker.