Runtime Verification of Temporal Properties over Out-of-order Data Streams

We present a monitoring approach for verifying systems at runtime. Our approach targets systems whose components communicate with the monitors over unreliable channels, where messages can be delayed or lost. In contrast to prior works, whose property specification languages are limited to propositional temporal logics, our approach handles an extension of the real-time logic MTL with freeze quantifiers for reasoning about data values. We present its underlying theory based on a new three-valued semantics that is well suited to soundly and completely reason online about event streams in the presence of message delay or loss. We also evaluate our approach experimentally. Our prototype implementation processes hundreds of events per second in settings where messages are received out of order.Comment: long version of the CAV 2017 pape

An Efficient Algorithm for Monitoring Practical TPTL Specifications

We provide a dynamic programming algorithm for the monitoring of a fragment of Timed Propositional Temporal Logic (TPTL) specifications. This fragment of TPTL, which is more expressive than Metric Temporal Logic, is characterized by independent time variables which enable the elicitation of complex real-time requirements. For this fragment, we provide an efficient polynomial time algorithm for off-line monitoring of finite traces. Finally, we provide experimental results on a prototype implementation of our tool in order to demonstrate the feasibility of using our tool in practical applications

Event Stream Processing with Multiple Threads

Current runtime verification tools seldom make use of multi-threading to speed up the evaluation of a property on a large event trace. In this paper, we present an extension to the BeepBeep 3 event stream engine that allows the use of multiple threads during the evaluation of a query. Various parallelization strategies are presented and described on simple examples. The implementation of these strategies is then evaluated empirically on a sample of problems. Compared to the previous, single-threaded version of the BeepBeep engine, the allocation of just a few threads to specific portions of a query provides dramatic improvement in terms of running time

Runtime Verification of Traces Under Recording Uncertainty

We present an on-line algorithm for the runtime checking of temporal properties, expressed as past-time Linear Temporal Logic (LTL) over the traces of observations recorded by a black box -like device. The recorder captures the observed values but not the precise time of their occurrences, and precise truth evaluation of a temporal logic formula cannot always be obtained. In order to handle this uncertainty, the checking algorithm is based on a three-valued semantics for pasttime LTL defined in this paper. In addition to the algorithm, the paper presents results of an evaluation that aimed to study the effects of the recording uncertainty on different kinds of temporal logic properties

From LTL to rLTL monitoring

Runtime monitoring is commonly used to detect the violation of desired properties in safety critical systems by observing run prefixes of the system. Bauer et al. introduced an influential framework for monitoring Linear Temporal Logic (LTL) properties, which is based on a three-valued semantics: the formula is already satisfied by the given prefix, it is already violated, or it is still undetermined, i.e., it can be satisfied and violated. However, a wide range of formulas are not monitorable under this approach, meaning that every prefix is undetermined. In particular, Bauer et al. report that 44% of the formulas they consider in their experiments fall into this category. Recently, robust semantics for LTL were introduced to capture degrees of violation of universal properties. Here, we define robust semantics for run prefixes and show its potential in monitoring: every formula considered by Bauer et al. is monitorable under our approach. Furthermore, we show that properties expressed with the robust semantics can be monitored by deterministic automata