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In this paper, we study the problem of runtime verification of real-time event streams; in particular, we propose a language to describe monitors for real-time event streams that can manipulate data from rich domains. We propose a solution based on stream runtime verification (SRV), where monitors are specified by describing how output streams of data are computed from input streams of data. SRV enables a clean separation between the temporal dependencies among incoming events and the concrete operations that are performed during the monitoring. Most SRV specification languages assume that all streams share a global synchronous clock and divide time in discrete instants. At each instant every input has a reading, and for every instant the monitor computes an output. In this paper, we generalize the time assumption to cover real-time event streams, but keep the explicit time offsets present in some synchronous SRV languages like Lola. The language we introduce, called Striver, shares with SRV the simplicity and economy of operators, and the separation between the reasoning about time and the computation of data values. The version of Striver in this paper allows expressing future and past dependencies. Striver is a general language that allows expressing for certain time domains other real-time monitoring languages, like TeSSLa, and temporal logics, like STL. We show in this paper translations from other formalisms for (piecewise-constant) real-time signals and timed event streams. Finally, we report an empirical evaluation of an implementation of Striver.

The ISO 26262 Road vehicles Functional Safety Standard is intended to guide the derivation of appropriate requirements and processes for avoiding systematic and/or random failures in automotive electrical/electronic equipment. Functional safety statements can be captured in the requirements specifications for automotive embedded control units and systems. However, the process of verifying the behaviour of resulting products continues to be incomplete; because embedded programme verification is unsolvable in general. This study shows that it is possible to monitor some proof obligations in the testing phase, or even in the actual operating phase of a system by the use of an on-chip, real-time runtime verification monitor. In this work, the ISO 26262 standard for functional safety is used to guide the definition of the functional safety requirements for a product, and the specific requirements are mapped to logic formulae, such that the actual runtime behaviour of the system for selected properties can be formally verified throughout the lifetime of a product. A case study example for an automotive gearbox control system is presented to demonstrate the feasibility of the scheme. The monitor is constructed as a permanent feature within an integrated circuit that can continuously observe the system's runtime behaviour.

Over the last 20 years, runtime verification (RV) has grown into a diverse and active field, which has stimulated the development of numerous theoretical frameworks and practical tools. Many of the tools are at first sight very different and challenging to compare. Yet, there are similarities. In this work, we classify RV tools within a high-level taxonomy of concepts. We first present this taxonomy and discuss its different dimensions. Then, we survey the existing RV tools and, where possible with the support of tool authors, classify them according to the taxonomy. While the classification continually evolves, this article presents a snapshot with 60 state-of-the-art RV tools. We believe that this work is an important step in establishing a common terminology in RV and enabling a meaningful comparison of existing RV tools.

With the gradual popularization of Internet-of-Things (IoT) applications and the development of wireless networking technologies, the use of heterogeneous devices and runtime verification of task fulfillment with different constraints are required in real-world IoT scenarios. As far as IoT systems are concerned, most of them are built on centralized architectures, which reveal various assailable points in data security and privacy threats. Hence, this paper aims to investigate these issues by delegating the responsibility of a verification monitor from a centralized architecture to a decentralized manner using blockchain technology. We present a smart contract-based task management scheme to provide runtime verification of device behaviors and allows trustworthy access control to these devices. The business logic of the proposed system is specified by the smart contract, which automates all time-consuming processes cryptographically and correctly. The usability of the proposed solution is further demonstrated by implementing a prototype application in which the Hyperledger Fabric is utilized to implement the business logic for runtime verification and access control with one desktop and one Raspberry Pi. A comprehensive evaluation experiment is conducted, and the results indicate the effectiveness and efficiency of the proposed system.

This article gives an overview of the, monitoring oriented programming framework (MOP). In MOP, runtime monitoring is supported and encouraged as a fundamental principle for building reliable systems. Monitors are automatically synthesized from specified properties and are used in conjunction with the original system to check its dynamic behaviors. When a specification is violated or validated at runtime, user-defined actions will be triggered, which can be any code, such as information logging or runtime recovery. Two instances of MOP are presented: JavaMOP (for Java programs) and BusMOP (for monitoring PCI bus traffic). The architecture of MOP is discussed, and an explanation of parametric trace monitoring and its implementation is given. A comprehensive evaluation of JavaMOP attests to its efficiency, especially in comparison with similar systems. The implementation of BusMOP is discussed in detail. In general, BusMOP imposes no runtime overhead on the system it is monitoring.

The first international Competition on Runtime Verification (CRV) was held in September 2014, in Toronto, Canada, as a satellite event of the 14th international conference on Runtime Verification (RV’14). The event was organized in three tracks: (1) offline monitoring, (2) online monitoring of C programs, and (3) online monitoring of Java programs. In this paper, we report on the phases and rules, a description of the participating teams and their submitted benchmark, the (full) results, as well as the lessons learned from the competition.

In this paper, we present the Real-Time Analog Monitoring Tool (RTAMT), a tool for quantitative monitoring of Signal Temporal Logic (STL) specifications. The library implements a flexible architecture that supports: (1) various environments connected by an Application Programming Interface (API) in Python, (2) various flavors of temporal logic specification and robustness notion such as STL, including an interface-aware variant that distinguishes between input and output variables, and (3) discrete-time and dense-time interpretation of STL with generation of online and offline monitors. We specifically focus on robotics and Cyber-Physical System (CPS) applications, showing how to integrate RTAMT into (1) the Robot Operating System (ROS) and (2) MATLAB/Simulink ® environments. We evaluate the tool by demonstrating several use scenarios involving service robotic and avionic applications.

The automotive industry is increasingly dependent on computing systems with different critical requirements. The verification and validation methods for these systems are now leveraging complex AI methods, for which the decision algorithms introduce non-determinism, especially in autonomous driving. This paper presents a runtime verification technique agnostic to the target system, which focuses on monitoring spatio-temporal properties that abstract the evolution of objects’ behavior in their spatial and temporal flow. First, a formalization of three known traffic rules (from the Vienna convention on road traffic) is presented, where a spatio-temporal logic fragment is used. Then, these logical expressions are translated to a monitoring model written in first-order logic, where they are processed by a non-linear satisfiability solver. Finally, the translation allows the solver to check the validity of the encoded properties according to an instance of a specific traffic scenario (a trace). The results obtained from our tool, which automatically generates a monitor from a formula, show that our approach is feasible for online monitoring in a real-world environment.

As a novel decentralized computing paradigm, blockchain is expected to disrupt the existing e-commerce architecture and process. Secure smart contracts are the crucial foundation for e-commerce based on blockchain. However, vulnerabilities in smart contracts occur from time to time and cause significant financial losses in e-commerce. Some static verification methods have been developed to guarantee security for e-commerce smart contracts at design time, but they cannot support complex scenarios at runtime. As a lightweight verification method, runtime verification is a potential method for secure e-commerce smart contracts. The existing runtime verification methods are based on the manual instrument, which leads to additional overheads and gas consumption. To deal with this, we propose a passive learning-based runtime verification framework for e-commerce smart contracts. Firstly, by exploring the Genetic algorithm to evolve state merging and automaton reorganizing in order to simultaneously split time and gas behaviors, we propose a passive learning method to model runtime information for e-commerce smart contracts (PL4ESC). It directly learns P2TA (priced probabilistic timed automaton) from runtime traces without any prior knowledge. Then, we integrate PL4ESC with the open-source PAT (Process Analysis Toolkit) to automatically verify the security of runtime e-commerce smart contracts. The experiments show that PL4ESC is better at accuracy and precision than state-of-the-art passive learning methods. It improves accuracy by 1 to 4 percent compared to TAG and RTI+. As far as we know, it is not only the first learning method that can learn a P2TA from traces, but it is also the first automated runtime verification framework for e-commerce smart contracts. This will provide security guarantees for blockchain-based e-commerce.

Many applications (such as system and user monitoring, runtime verification, diagnosis, observation-based decision making, intention recognition) all require to detect the occurrence of an event in a system, which entails the ability to observe the system. Observation can be costly, so it makes sense to try and reduce the number of observations, without losing full certainty about the event’s actual occurrence. In this paper, we propose a formalization of this problem. We formally show that, whenever the event to be detected follows a discrete spatial or temporal pattern, then it is possible to reduce the number of observations. We discuss exact and approximate algorithms to solve the problem, and provide an experimental evaluation of them. We apply the resulting algorithms to verification of linear temporal logics formulæ. Finally, we discuss possible generalizations and extensions, and, in particular, how event detection can benefit from logic programming techniques.