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Many Cyber-Physical Systems (CPS) are highly nondeterministic. This often makes it impractical to model and predict the complete system behavior. To address this problem, we propose that instead of offline modeling and verification, many CPS systems should be modeled and verified online, and we shall focus on the system's time-bounded behavior in short-run future, which is more describable and predictable. Meanwhile, as the system model is generated/updated online, the verification has to be fast. It is meaningless to tell an online model is unsafe when it is already outdated. To demonstrate the feasibility of our proposal, we study two cases of our ongoing projects, one on the modeling and verification of a train control system, and the other on a Medical Device Plug-and-Play (MDPnP) application. Both cases are about safetycritical CPS systems. Through these two cases, we exemplify how to build online models that describe the time-bounded short-run behavior of CPS systems; and we show that fast online modeling and verification is possible.

Hybrid automata are well studied formal models for hybrid systems with both discrete and continuous state changes. However, the analysis of hybrid automata is quite difficult. Even for the simple class of linear hybrid automata, the reachability problem is undecidable. In the author's previous work, for linear hybrid automata we proposed a linear programming based approach to check one path at a time while the length of the path and the size of the automaton being checked can be large enough to handle problems of practical interest. Based on this approach, in this paper we present a prototype tool BACH to perform bounded reachability checking of linear hybrid automata. The experiment data shows that BACH has good performance and scalability, and supports our belief that BACH could become a powerful assistant to design engineers for the reachability analysis of linear hybrid automata.

Recent advances and industry standards in Internet of Things (IoT) have accelerated the real-world adoption of connected devices. To manage this hybrid system of digital real-time devices and analog environments, the industry has pushed several popular home automation IoT (HA-IoT) frameworks, such as If-This-Then-That (IFTTT), Apple HomeKit, and Google Brillo. Typically, users author device interactions by specifying the triggering sensor event and the triggered device command. In this seemingly simple software system, two dominant factors govern the system confidence properties with respect to the physical world. First, IoT users are largely nonexperts who lack the comprehensive consideration regarding potential impact and joint effect with existing rules. Second, while the increasing complexity of IoT devices enables fine-grained control (e.g., heater temperature) of continuous real-time environments, even two simply connected devices can have a huge state space to explore. In fact, bugs that wrongfully control devices and home appliances can have ramifications on system correctness and even user physical safety. It is crucial to help users to make sure the system they created meets their expectation. In this article we introduce how techniques from hybrid automata can be practically applied to assist nonexpert IoT users in the confidence checking of such hybrid HA-IoT systems. We propose an automated framework for end-to-end programming assistance. We build and check the Linear Hybrid Automata (LHA) model of the system automatically. We also present a quantifier elimination-based method to analyze the counterexample found and synthesize fix suggestions. We implemented a platform, MenShen, based on this framework and proposed techniques. We conducted sets of real HA-IoT case studies with up to 46 devices and 65 rules. Empirical results show that MenShen can find violations and generate rule fix suggestions in only 10 seconds.

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