Conference
Runtime Verification
About: Runtime Verification is an academic conference. The conference publishes majorly in the area(s): Runtime verification & Computer science. Over the lifetime, 468 publications have been published by the conference receiving 10261 citations.
Papers published on a yearly basis
Papers
1 Nov 2010
TL;DR: The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas as discussed by the authors.
Abstract: Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties Another approach to solve the model checking problem is to simulate the system for finitely many executions, and use hypothesis testing to infer whether the samples provide a statistical evidence for the satisfaction or violation of the specification In this tutorial, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity
529 citations
1 Jan 2018
TL;DR: This chapter summarise the state-of-the-art techniques for qualitative and quantitative monitoring of CPS behaviours, and presents an overview of some of the important applications and describes the tools supporting CPS monitoring and compare their main features.
Abstract: The term Cyber-Physical Systems (CPS) typically refers to engineered, physical and biological systems monitored and/or controlled by an embedded computational core. The behaviour of a CPS over time is generally characterised by the evolution of physical quantities, and discrete software and hardware states. In general, these can be mathematically modelled by the evolution of continuous state variables for the physical components interleaved with discrete events. Despite large effort and progress in the exhaustive verification of such hybrid systems, the complexity of CPS models limits formal verification of safety of their behaviour only to small instances. An alternative approach, closer to the practice of simulation and testing, is to monitor and to predict CPS behaviours at simulation-time or at runtime. In this chapter, we summarise the state-of-the-art techniques for qualitative and quantitative monitoring of CPS behaviours. We present an overview of some of the important applications and, finally, we describe the tools supporting CPS monitoring and compare their main features.
374 citations
1 Oct 2001
TL;DR: Recent work on the development of Java PathExplorer (\JPaXX), a tool for monitoring the execution of Java programs, can be used during program testing to gain increased information about program executions, and can potentially furthermore be applied during operation to survey safety critical systems.
Abstract: We present recent work on the development of Java PathExplorer (JP a X), a tool for monitoring the execution of Java programs. JPaX can be used during program testing to gain increased information about program executions, and can potentially furthermore be applied during operation to survey safety critical systems. The tool facilitates automated instrumentation of a program's byte code, which will then emit events to an observer during its execution. The observer checks the events against user provided high level requirement specifications, for example temporal logic formulae, and against lower level error detection procedures, usually concurrency related such as deadlock and data race algorithms. High level requirement specifications together with their underlying logics are defined in rewriting logic using Maude, and then can either be directly checked using Maude rewriting engine, or be first translated to efficient data structures and then checked in Java.
311 citations
1 Jan 2018
TL;DR: The aim of this chapter is to act as a primer for those wanting to learn about Runtime Verification, providing an overview of the main specification languages used for RV and introducing the standard terminology necessary to describe the monitoring problem.
Abstract: The aim of this chapter is to act as a primer for those wanting to learn about Runtime Verification (RV) We start by providing an overview of the main specification languages used for RV We then introduce the standard terminology necessary to describe the monitoring problem, covering the pragmatic issues of monitoring and instrumentation, and discussing extensively the monitorability problem
282 citations
22 Sep 2014
TL;DR: The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity, thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information.
Abstract: In this paper we present TTT, a novel active automata learning algorithm formulated in the Minimally Adequate Teacher (MAT) framework. The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity. This is thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information. TTT is therefore particularly well-suited for application in a runtime verification context, where counterexamples (obtained, e.g., via monitoring) may be excessively long: as the execution time of a test sequence typically grows with its length, this would otherwise cause severe performance degradation. We illustrate the impact of TTT’s consequent redundancy-free approach along a number of examples.
213 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2022 | 23 |
| 2021 | 18 |
| 2020 | 28 |
| 2019 | 25 |
| 2018 | 36 |
| 2017 | 47 |