Testing Techniques in IoT-based Systems

Medhat, Noha; Moussa, Sherin M.; Badr, Nagwa L.; Tolba, Mohamed F.

doi:10.1109/icicis46948.2019.9014711

Cited by 15 publications

(9 citation statements)

References 48 publications

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“…Testing research in IoT-based systems has addressed the need for improved testing techniques to match the nature of such systems. The challenges of testing in IoT-based systems were addressed in [1] on the different testing levels, where a lackage was indicated when testing IoT-based systems during the selection and prioritization of the IoT system TCs . In [6], a testing technique was proposed in order to effectively apply integration and regression testing over IoT based systems, which was by merging deep learning LSTM algorithm for the IoT TCs selection.…”

Section: Related Workmentioning

confidence: 99%

“…Where is for the test case identity that starts from until it reaches the total number of TCs in the sequence of TCs that the fitness function is being calculated for, the test case is for the order of test case in the sequence, is the coverage rate that defines the rate of the requirements covered using the specified test case, is the fault detection rate that is being detected by the specified test case, is the execution time each test case requires to run. The sum of the coverage rate and the fault detection rate is divided over the execution time which is defined as in (1).…”

Section: Iot Selected Test Cases Prioritizationmentioning

confidence: 99%

“…It checks the current sequence of TCs with the nearest sequences of TCs to determine if the current sequence is better than the next or previous sequence, deciding whether to keep the current sequence as the best prioritized sequence or to move to the next or previous sequence. The comparison between the sequences is based on the FF calculation as shown in (1). Reaching a better solution than the current one is defined as the Local Maxima [22], in which the solution is either moving forward or backtrack in order to reach the local maxima.…”

Section: Iot Selected Test Cases Prioritizationmentioning

confidence: 99%

“…Internet of Things (IoT) based systems differ from traditional systems, in which an enoromous number of heterogenous devices are connected to build IoT-based systems, raising various difficulities and challenges in different aspects. The issues faced are due to the incremental addition of devices, users, sensors, using different technologies and protocols [1] [2]. Focusing on testing issues demands high investigation to ensure the effectiveness and validity of IoT-based systems, pushing to find new solutions that are capable of handling the unique nature of IoT-based systems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhancing Test Cases Prioritization for Internet of Things based systems using Search-based Technique

Mohamed

Moussa

Badr

et al. 2021

IJICIS

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Test cases prioritization has been excessively considered for continious regression and integration testing in Internet of Things based systems to apply multilevel testing activities. Various number of devices, sensors and acctuators are connected together through the internet using different technologies, which requires extensively testing the effeciency of these components and the transferred data between them. Due to the number of the connected components has dramatically increased, causing a direct proportional increase in the number of test cases.Studies that handle the augmentation of the number of test cases for traditional systems lack effeciency when applied for Internet of Things based systems. In this paper, we introduce an enhancement for test cases prioritization using Hill Climbing algorithm as a local search based technique, adapted to achieve tangible effeciency. It is integrated with the LSTM deep learning algorithm for test cases classification purposes. The results of the test cases prioritization using Hill Climbing for regression and integration testing are evaluated using precision, where it achieved 80% and 97% for regression testing, and 93% and 88% for integration testing using two Internet of Things-based system datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Iot Selected Test Cases Prioritizationmentioning

confidence: 99%

Section: Iot Selected Test Cases Prioritizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Test Cases Prioritization for Internet of Things based systems using Search-based Technique

Mohamed

Moussa

Badr

et al. 2021

IJICIS

Self Cite

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“…Then through the network layer, different protocols and gateways are used for data transition to apply analytical processes, providing appropriate services to the targeted user's applications [2]. Testing is required at all stages, when everything is connected, and data are transmitted through networks [3]. Issues and threats are increasingly faced, especially regarding the privacy of critical personal data in order to maintain security and performance processes [4].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques

et al. 2020

Self Cite

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Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT system, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems. In this paper, a scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.

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Interoperability and Integration Testing Methods for IoT Systems: A Systematic Mapping Study

Bureš

Klima

Rechtberger

et al. 2020

Lecture Notes in Computer Science

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Testing Techniques in IoT-based Systems

Cited by 15 publications

References 48 publications

Enhancing Test Cases Prioritization for Internet of Things based systems using Search-based Technique

Enhancing Test Cases Prioritization for Internet of Things based systems using Search-based Technique

A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques

Interoperability and Integration Testing Methods for IoT Systems: A Systematic Mapping Study

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