Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Unfolding-based Partial Order Reduction

Partial order reduction (POR) and net unfoldings are two alternative methods to tackle state-space explosion caused by concurrency. In this paper, we propose the combination of both approaches in an effort to combine their strengths. We first define, for an abstract execution model, unfolding semantics parameterized over an arbitrary independence relation. Based on it, our main contribution is a novel stateless POR algorithm that explores at most one execution per Mazurkiewicz trace, and in general, can explore exponentially fewer, thus achieving a form of super-optimality. Furthermore, our unfolding-based POR copes with non-terminating executions and incorporates state-caching. Over benchmarks with busy-waits, among others, our experiments show a dramatic reduction in the number of executions when compared to a state-of-the-art DPOR.Comment: Long version of a paper with the same title appeared on the proceedings of CONCUR 201

Parameterized Reachability Graph for Software Model Checking Based on PDNet

Model checking is a software automation verification technique. However, the complex execution process of concurrent software systems and the exhaustive search of state space make the model-checking technique limited by the state-explosion problem in real applications. Due to the uncertain input information (called system parameterization) in concurrent software systems, the state-explosion problem in model checking is exacerbated. To address the problem that reachability graphs of Petri net are difficult to construct and cannot be explored exhaustively due to system parameterization, this paper introduces parameterized variables into the program dependence net (a concurrent program model). Then, it proposes a parameterized reachability graph generation algorithm, including decision algorithms for verifying the properties. We implement LTL-x verification based on parameterized reachability graphs and solve the problem of difficulty constructing reachability graphs caused by uncertain inputs

Doctor of Philosophy

dissertationOver the last decade, cyber-physical systems (CPSs) have seen significant applications in many safety-critical areas, such as autonomous automotive systems, automatic pilot avionics, wireless sensor networks, etc. A Cps uses networked embedded computers to monitor and control physical processes. The motivating example for this dissertation is the use of fault- tolerant routing protocol for a Network-on-Chip (NoC) architecture that connects electronic control units (Ecus) to regulate sensors and actuators in a vehicle. With a network allowing Ecus to communicate with each other, it is possible for them to share processing power to improve performance. In addition, networked Ecus enable flexible mapping to physical processes (e.g., sensors, actuators), which increases resilience to Ecu failures by reassigning physical processes to spare Ecus. For the on-chip routing protocol, the ability to tolerate network faults is important for hardware reconfiguration to maintain the normal operation of a system. Adding a fault-tolerance feature in a routing protocol, however, increases its design complexity, making it prone to many functional problems. Formal verification techniques are therefore needed to verify its correctness. This dissertation proposes a link-fault-tolerant, multiflit wormhole routing algorithm, and its formal modeling and verification using two different methodologies. An improvement upon the previously published fault-tolerant routing algorithm, a link-fault routing algorithm is proposed to relax the unrealistic node-fault assumptions of these algorithms, while avoiding deadlock conservatively by appropriately dropping network packets. This routing algorithm, together with its routing architecture, is then modeled in a process-algebra language LNT, and compositional verification techniques are used to verify its key functional properties. As a comparison, it is modeled using channel-level VHDL which is compiled to labeled Petri-nets (LPNs). Algorithms for a partial order reduction method on LPNs are given. An optimal result is obtained from heuristics that trace back on LPNs to find causally related enabled predecessor transitions. Key observations are made from the comparison between these two verification methodologies

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.