Safe and efficient runtime testing framework applied in dynamic and distributed systems

Lahami, Mariam; Krichen, Moez; Jmaïel, Mohamed

doi:10.1016/j.scico.2016.02.002

Cited by 32 publications

(23 citation statements)

References 9 publications

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“…In the near future, we would like to demonstrate our approach by experiences the application of deep reinforcement learning methods in autonomous robots, especially for autonomous car driving scenarios and our focus will also be to propose some novel and Intelligent IoT applications integrating the multi-agent systems with intelligent edge computing using machine learning. In addition, we will work on adapting existing formal approaches [9,[16][17][18][19][20] for testing and validating our framework.…”

Section: Resultsmentioning

confidence: 99%

Towards an Agent-Based Architecture using Deep Reinforcement Learning for Intelligent Internet of Things Applications.pdf

Noureddine¹,

Krichen²,

Mechti³

et al. 2020

Preprint

Self Cite

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Internet of Things (IoT) is composed of many IoT devices connected throughout the Internet, that collect and share information to represent the environment. IoT is currently restructuring the actual manufacturing to smart manufacturing. However, inherent characteristics of IoT lead to a number of titanic challenges such as decentralization, weak interoperability, security, etc. The artificial intelligence provides opportunities to address IoT’s challenges, e.g the agent technology. This paper presents first an overview of ML and discusses some related work. Then, we briefly present the classic IoT architecture. Then we introduce our proposed Intelligent IoT (IIoT) architecture. We next concentrate on introducing the approach using multi-agent DRL in IIoT. Finally, in this promising field, we outline the open directions of future work.

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Section: Resultsmentioning

confidence: 99%

Towards an Agent-Based Architecture using Deep Reinforcement Learning for Intelligent Internet of Things Applications.pdf

Noureddine¹,

Krichen²,

Mechti³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This problem is inspired by fog computing approaches (Taneja and Davy, 2017;Gu et al, 2017) and by some of our previous contributions (Maâlej et al, 2018;Lahami et al, 2016;Lahami et al, 2012b). It consists in allocating the set of testers on the different computational nodes of the SUT in an optimal manner under several types of constraints as mentioned below.…”

Section: Optimization Of Testers Placementmentioning

confidence: 99%

“…Besides, we propose an approach for the optimization of the testers placement procedure inspired by fog computing approaches (Taneja and Davy, 2017;Gu et al, 2017;Mahmud et al, 2018;Barcelo et al, 2016) and by some of our previous contributions (Maâlej et al, 2018;Lahami et al, 2016;Lahami et al, 2012b). This placement procedure consists in allocating the set of testers on the different computational nodes of the system under test in an optimal way under different kind of constraints.…”

Section: Introductionmentioning

confidence: 99%

A Model-Based Testing Framework for Validating an IoT Solution for Blockchain-Based Vehicles Communication

Jabbar¹,

Krichen²,

Kharbeche³

et al. 2020

Preprint

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<div>The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept of smart cities by connecting every ``thing'' to the Internet and in particular in transportation through the Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving traffic and road safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data in ``Vehicle-to-Vehicle'' (V2V) and ``Vehicle-to-Infrastructure'' (V2I) communication. </div><div>To improve ``Vehicle-to-Everything'' (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing platform.</div><div> Moreover, the authors propose a model-based framework to validate the proposed approach. This framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog computing is proposed as well.</div>

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“…Duplication (also called Cloning) [6], [8], [17] consists of cloning the execution state (e.g., by forking a parallel process [17]) and executing in-vivo tests on the cloned execution state, hence ensuring that there is no interference with the end user execution of the application (in-memory side effects are prevented, but of course other side effects on persistent storage are not dealt with). Another proposed isolation mechanism is Test mode execution [3], [10], [12], [14], [21]. It requires a way to differentiate between the execution of a component in normal operation mode vs. the testing mode.…”

Section: Related Workmentioning

confidence: 99%

Toward In-Vivo Testing of Mobile Applications

Ceccato

Gazzola

Kifetew

et al. 2019

2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)

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The ecosystem in which mobile applications run is highly heterogeneous and configurable. All layers upon which mobile apps are built offer wide possibilities of variations, from the device and the hardware, to the operating system and middleware, up to the user preferences and settings. Testing all possible configurations exhaustively, before releasing the app, is unaffordable. As a consequence, the app may exhibit different, including faulty, behaviours when executed in the field, under specific configurations.In this paper, we describe a framework that can be instantiated to support in-vivo testing of a mobile app. The framework monitors the configuration in the field and triggers in-vivo testing when an untested configuration is recognized. Experimental results show that the overhead introduced by monitoring is unnoticeable to negligible (i.e., 0-6%) depending on the device being used (high-vs. low-end). In-vivo test execution required on average 3s: if performed upon screen lock activation, it introduces just a slight delay before locking the device.

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Safe and efficient runtime testing framework applied in dynamic and distributed systems

Cited by 32 publications

References 9 publications

Towards an Agent-Based Architecture using Deep Reinforcement Learning for Intelligent Internet of Things Applications.pdf

Towards an Agent-Based Architecture using Deep Reinforcement Learning for Intelligent Internet of Things Applications.pdf

A Model-Based Testing Framework for Validating an IoT Solution for Blockchain-Based Vehicles Communication

Toward In-Vivo Testing of Mobile Applications

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