From Passive to Active: Learning Timed Automata Efficiently

Aichernig, Bernhard K.; Pferscher, Andrea; Tappler, Martin

doi:10.1007/978-3-030-55754-6_1

Cited by 24 publications

(13 citation statements)

References 29 publications

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“…These variables store time values and, therefore, represent real‐time clocks. The clock values increase when time elapses 14 . Figure 2 depicts an example of a timed automaton.…”

Section: Backgroundsmentioning

confidence: 99%

Improving consistency of AADL models: A composition approach

Hamdane

Harous

Kerkouche

et al. 2023

Systems Engineering

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AADL—architecture analysis and design language—has proven to be important in developing real‐time embedded systems. However, its formalization capability is still limited, which makes it difficult to produce a consistent model; particularly, in the case where several models are combined together. Therefore, using formal verification is an effective way to meet the consistency requirements of the AADL model. This paper proposes a model‐based engineering approach that uses timed automata (TA) formalism to aggregate multiple AADL models into a single composite model. Based on the set of input AADL models, the proposed approach creates a consistent composed model as well as support model checking verification. The effectiveness of this contribution is evaluated through two case studies; a multicamera surveillance system and a drone system.

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“…These variables store time values and, therefore, represent real‐time clocks. The clock values increase when time elapses 14 . Figure 2 depicts an example of a timed automaton.…”

Section: Backgroundsmentioning

confidence: 99%

Improving consistency of AADL models: A composition approach

Hamdane

Harous

Kerkouche

et al. 2023

Systems Engineering

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“…kTail [6], also considering sparse data. A comparison of passive and active learning was performed by Aichernig et al [2]. In their work, they compared a passive and an active variant of a search-based learning algorithm for timed automata.…”

Section: Related Workmentioning

confidence: 99%

Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols

Aichernig

Muškardin

Pferscher

2022

Electron. Proc. Theor. Comput. Sci.

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Active automata learning became a popular tool for the behavioral analysis of communication protocols. The main advantage is that no manual modeling effort is required since a behavioral model is automatically inferred from a black-box system. However, several real-world applications of this technique show that the overhead for the establishment of an active interface might hamper the practical applicability. Our recent work on the active learning of Bluetooth Low Energy (BLE) protocol found that the active interaction creates a bottleneck during learning. Considering the automata learning toolset, passive learning techniques appear as a promising solution since they do not require an active interface to the system under learning. Instead, models are learned based on a given data set. In this paper, we evaluate passive learning for two network protocols: BLE and Message Queuing Telemetry Transport (MQTT). Our results show that passive techniques can correctly learn with less data than required by active learning. However, a general random data generation for passive learning is more expensive compared to the costs of active learning.

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“…If large datasets of traces are available, stochastic learning of hybrid automata is feasible. Tappler et al [20] use genetic programming to reconstruct timed automata both in an offline and online setting [2]. Santana et al [16] build hybrid automata with the Expectation-Maximization algorithm to iteratively define the model parameters.…”

Section: Related Workmentioning

confidence: 99%

Conservative Hybrid Automata from Development Artifacts

Niklas¹,

Schmitt²,

Schwenger³

2021

Preprint

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The verification of cyber-physical systems operating in a safetycritical environment requires formal system models. The validity of the verification hinges on the precision of the model: possible behavior not captured in the model can result in formally verified, but unsafe systems. Yet, manual construction is delicate and errorprone while automatic construction does not scale for large and complex systems. As a remedy, this paper devises an automatic construction algorithm that utilizes information contained in artifacts of the development process: a runtime monitoring specification and recorded test traces. These artifacts incur no additional cost and provide sufficient information so that the construction process scales well for large systems. The algorithm uses a hybrid approach between a top-down and a bottom-up construction which allows for proving the result conservative, while limiting the level of over-approximation. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems; • Theory of computation → Timed and hybrid models; Formal languages and automata theory.

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From Passive to Active: Learning Timed Automata Efficiently

Cited by 24 publications

References 29 publications

Improving consistency of AADL models: A composition approach

Improving consistency of AADL models: A composition approach

Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols

Conservative Hybrid Automata from Development Artifacts

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