Compensation-aware runtime monitoring

To avoid large overheads induced by runtime monitoring, the use of asynchronous log-based monitoring is sometimes adopted — even though this implies that the system may proceed further despite having reached an anomalous state. Any actions performed by the system after the error occurring are undesirable, since for instance, an unchecked malicious user may perform unauthorized actions. Since stopping such actions is not feasible, in this paper we investigate the use of compensations to enable the undoing of actions, thus enriching asynchronous monitoring with the ability to restore the system to the original state in which the anomaly occurred. Furthermore, we show how allowing the monitor to adaptively synchronise and desynchronise with the system is also possible and report on the use of the approach on an industrial case study of a financial transaction system.peer-reviewe

Extensible Technology-Agnostic Runtime Verification

With numerous specialised technologies available to industry, it has become increasingly frequent for computer systems to be composed of heterogeneous components built over, and using, different technologies and languages. While this enables developers to use the appropriate technologies for specific contexts, it becomes more challenging to ensure the correctness of the overall system. In this paper we propose a framework to enable extensible technology agnostic runtime verification and we present an extension of polyLarva, a runtime-verification tool able to handle the monitoring of heterogeneous-component systems. The approach is then applied to a case study of a component-based artefact using different technologies, namely C and Java.Comment: In Proceedings FESCA 2013, arXiv:1302.478

Bayesian Verification under Model Uncertainty

Machine learning enables systems to build and update domain models based on runtime observations. In this paper, we study statistical model checking and runtime verification for systems with this ability. Two challenges arise: (1) Models built from limited runtime data yield uncertainty to be dealt with. (2) There is no definition of satisfaction w.r.t. uncertain hypotheses. We propose such a definition of subjective satisfaction based on recently introduced satisfaction functions. We also propose the BV algorithm as a Bayesian solution to runtime verification of subjective satisfaction under model uncertainty. BV provides user-definable stochastic bounds for type I and II errors. We discuss empirical results from an example application to illustrate our ideas.Comment: Accepted at SEsCPS @ ICSE 201

Requirement verification in simulation-based automation testing

The emergence of the Industrial Internet results in an increasing number of complicated temporal interdependencies between automation systems and the processes to be controlled. There is a need for verification methods that scale better than formal verification methods and which are more exact than testing. Simulation-based runtime verification is proposed as such a method, and an application of Metric temporal logic is presented as a contribution. The practical scalability of the proposed approach is validated against a production process designed by an industrial partner, resulting in the discovery of requirement violations.Comment: 4 pages, 2 figures. Added IEEE copyright notic