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Techniques for runtime verification often utilise specification languages that are (i) reasonably expressive, and (ii) relatively abstract (i.e. they operate on a level of abstraction that separates them from the system being monitored). Inspired by the problem of monitoring systems involved in processing data generated by the high energy physics experiments at CERN, this report proposes a specification language, Control Flow Temporal Logic (CFTL), whose distinguishing characteristic is its tight coupling with the control flow of the programs for which it is used to write specifications. This coupling leads to a departure from the typically high level of abstraction used by most temporal logics. The remaining contributions are a static-analysis based instrumentation process, which is specific to CFTL and its formulas structure, and a monitoring algorithm. The report concludes with analyses of CFTL and its monitoring algorithm when applied to a number of example programs.
This paper presents two formal models of the Data Encryption Standard (DES), a first using the international standard LOTOS, and a second using the more recent process calculus LNT. Both models encode the DES in the style of asynchronous circuits, i.
We present a faster symbolic algorithm for the following central problem in probabilistic verification: Compute the maximal end-component (MEC) decomposition of Markov decision processes (MDPs). This problem generalizes the SCC decomposition problem
We propose automated techniques for the verification and control of probabilistic real-time systems that are only partially observable. To formally model such systems, we define an extension of probabilistic timed automata in which local states are p
Time-Sensitive Distributed Systems (TSDS), such as applications using autonomous drones, achieve goals under possible environment interference (eg, winds). Moreover, goals are often specified using explicit time constraints which must be satisfied by
We investigate the problem of monitoring partially observable systems with nondeterministic and probabilistic dynamics. In such systems, every state may be associated with a risk, e.g., the probability of an imminent crash. During runtime, we obtain