Mining Constraints for Event-based Monitoring in Systems of Systems

Thomas KrismayerRick Rabiser, Paul Grünbacher



Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, 2017

The full behavior of software-intensive systems of systems (SoS) emerges during operation only. Runtime monitoring approaches have thus been proposed to detect deviations from the expected behavior. They commonly rely on temporal logic or domain-specific languages to formally define requirements, which are then checked by analyzing the stream of monitored events and event data. Some approaches also allow developers to generate constraints from declarative specifications of the expected behavior. However, independent of the approach, deep domain knowledge is required to specify the desired behavior. This knowledge is often not accessible in SoS environments with multiple development teams independently working on different, heterogeneous systems. In this New Ideas Paper we thus describe an approach that automatically mines constraints for runtime monitoring from event logs recorded in SoS. Our approach builds on ideas from specification mining, process mining, and machine learning to mine different types of constraints on event occurrence, event timing, and event data. The approach further presents the mined constraints to users in an existing constraint language and it ranks the constraints using different criteria. We demonstrate the feasibility of our approach by applying it to event logs from a real-world industrial SoS.