Trace Analysis Using an Event-Driven Interval Temporal Logic

Gallardo, María del Mar Prados; Panizo, Laura

doi:10.1007/978-3-030-45260-5_11

Cited by 1 publication

(2 citation statements)

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“…Figure 1 shows our proposal, which has been implemented in the tool STAn. The inputs of STAn are the non-functional properties described in the event-driven temporal logic (eLTL in short) (Gallardo and Panizo 2019), and the data trace under analysis, which is a sequence of observable system states that includes timestamps and sampled real-valued variables.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

“…In Gallardo and Panizo (2019), we presented the eLTL logic and a first version of the implementation. In the current paper, we extend this work with the following contributions: (1) the tool STAn that includes a new offline and online monitoring approaches;…”

Section: Introductionmentioning

confidence: 99%