Nested Monitors: Monitors as Expressions to Build Monitors

Gorostiaga, Felipe; Sánchez, César

doi:10.1007/978-3-030-88494-9_9

Cited by 5 publications

(1 citation statement)

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“…In eLTL, we could analyze a property ψ in a time window of fixed size with [ p,q] ψ formula, where p and q are events triggered at the beginning and end of each temporal window. On the other hand, some SRV tools (Hallé 2016;Volanschi and Serpette 2019;Gorostiaga and Sánchez 2021) implement monitors in such a way that they can support customized or user-defined operations to deal with their rich input language. In this sense, eLTL maintains a balance between the fixed set of operators and the interval formulae that can be defined by the user.…”

Section: Stream Runtime Verificationmentioning

confidence: 99%

STAn: analysis of data traces using an event-driven interval temporal logic

Panizo

Gallardo

2022

Autom Softw Eng

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The increasing integration of systems into people’s daily routines, especially smartphones, requires ensuring correctness of their functionality and even some performance requirements. Sometimes, we can only observe the interaction of the system (e.g. the smartphone) with its environment at certain time points; that is, we only have access to the data traces produced due to this interaction. This paper presents the tool STAn, which performs runtime verification on data traces that combine timestamped discrete events and sampled real-valued magnitudes. STAn uses the Spin model checker as the underlying execution engine, and analyzes traces against properties described in the so-called event-driven interval temporal logic () by transforming each formula into a network of concurrent automata, written in Promela, that monitors the trace. We present two different transformations for online and offline monitoring, respectively. Then, Spin explores the state space of the automata network and the trace to return a verdict about the corresponding property. We use the proposal to analyze data traces obtained during mobile application testing in different network scenarios.

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Section: Stream Runtime Verificationmentioning

confidence: 99%