Model Checking Time Window Temporal Logic for Hyperproperties

Hyperproperties extend trace properties to express properties of sets of traces, and they are increasingly popular in specifying various security and performance-related properties in domains such as cyber-physical systems, smart grids, and automotive. This paper introduces a model checking algorithm for a new formalism, HyperTWTL, which extends Time Window Temporal Logic (TWTL) -- a domain-specific formal specification language for robotics, by allowing explicit and simultaneous quantification over multiple execution traces. We present HyperTWTL with both \emph{synchronous} and \emph{asynchronous} semantics, based on the alignment of the timestamps in the traces. Consequently, we demonstrate the application of HyperTWTL in formalizing important information-flow security policies and concurrency for robotics applications. Finally, we propose a model checking algorithm for verifying fragments of HyperTWTL by reducing the problem to a TWTL model checking problem.

QTWTL: Quality Aware Time Window Temporal Logic for Performance Monitoring

In various service-oriented applications such as distributed autonomous delivery, healthcare, tourism, transportation, and many others, where service agents need to perform serial and time-bounded tasks to achieve their goals, quality of service must constantly be assured. In addition to safety requirements, such agents also need to fulfill performance requirements in order to satisfy their quality of service. This paper proposes the novel quality-aware time window temporal logic (QTWTL) by extending the traditional time window temporal logic (TWTL) with two operators for counting and aggregation operations. We also propose offline runtime monitoring algorithms for the performance monitoring of QTWTL specifications. To analyze the feasibility and efficiency of our proposed approach, we generate a large number of traces using the New York City Taxi and Limousine Commission Trip Record data, formalize their performance requirements using QTWTL, and monitor them using the proposed algorithms. The obtained results show that the monitoring algorithm has a linear space and time complexity with respect to the number of traces monitored.