Second International Competition on Runtime Verification: CRV 2015

International audienceWe report on the Second International Competition on Run-time Verification (CRV-2015). The competition was held as a satellite event of the 15th International Conference on Runtime Verification (RV'15). The competition consisted of three tracks: o✏ine monitoring, online monitoring of C programs, and online monitoring of Java programs. This report describes the format of the competition, the participating teams and submitted benchmarks. We give an example illustrating the two main inputs expected from the participating teams, namely a benchmark (i.e., a program and a property on this program) and a monitor for this benchmark. We also propose some reflection based on the lessons learned

Towards a hybrid approach to software verification

Despite its advantages, RV is limited when compared to MC because certain correctness properties cannot be verified at runtime [5, 10, 15]. For instance, MC makes it possible to check for both safety and liveness properties, by providing either a positive or a negative answer, according to whether the system conforms with the specifications; RV, on the other hand, can only return a positive verdict for certain liveness properties (called co-safety properties [5]) or a negative one for safety conditions. Moreover, RV induces a runtime overhead over the execution of a monitored system, which should ideally be kept to a minimum [14].peer-reviewe

LarvaStat : monitoring of statistical properties

Execution paths expose non-functional information such as system reliability and performance, which can be collected using runtime verification techniques. Statistics gathering and evaluation can be very useful for processing such information for areas ranging from performance profiling to user modelling and intrusion detection. In this paper, we give an overview of LarvaStat -- a runtime verification tool extending LARVA [2] with the ability to straight forwardly specify real-time related statistical properties. Being automaton-based, LarvaStat also makes explicit the overhead induced by monitoring.peer-reviewe

Statistics and runtime verification

The importance of correctness of systems is becoming more crucial as computers control more of our everyday activities. Various approaches have been advocated and used for the verification of such correctness, with one of the more promising ones being runtime verification. One important issue in runtime verification is the logic used to specify properties, since this influences both the overheads induced by the monitors, and the applicability of the approach to a particular domain. In this paper we propose techniques for the expression and runtime monitoring of statistical properties, enabling easier manipulation and expression of non-functional requirements. The logic is developed as an extension of the existing runtime verification tool LARVA, and has been applied to an ftp server implementation, adding a new layer of probabilistic intrusion detection and system profiling.peer-reviewe

NuRV: A nuXmv Extension for Runtime Verification

We present NuRV, an extension of the nuXmv model checker for assumption-based LTL runtime verification with partial observability and resets. The tool provides some new commands for online/offline monitoring and code generations into standalone monitor code. Using the online/offline monitor, LTL properties can be verified incrementally on finite traces from the system under scrutiny. The code generation currently supports C, C++, Common Lisp and Java, and is extensible. Furthermore, from the same internal monitor automaton, the monitor can be generated into SMV modules, whose characteristics can be verified by Model Checking using nuXmv. We show the architecture, functionalities and some use scenarios of NuRV, and we compare the performance of generated monitor code (in Java) with those generated by a similar tool, RV-Monitor. We show that, using a benchmark from Dwyer's LTL patterns, besides the capacity of generating monitors for long LTL formulae, our Java-based monitors are about 200x faster than RV-Monitor at generation-time and 2–5x faster at runtime