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MATTER: A tool for generating end-to-end IoT test scripts

In the last few years, Internet of Things (IoT) systems have drastically increased their relevance in many fundamental sectors. For this reason, assuring their quality is of paramount importance, especially in safety-critical contexts. Unfortunately, few quality assurance proposals for assuring the...

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Bibliographic Details
Published in:Software quality journal 2022-06, Vol.30 (2), p.389-423
Main Authors: Olianas, Dario, Leotta, Maurizio, Ricca, Filippo
Format: Article
Language:English
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Summary:In the last few years, Internet of Things (IoT) systems have drastically increased their relevance in many fundamental sectors. For this reason, assuring their quality is of paramount importance, especially in safety-critical contexts. Unfortunately, few quality assurance proposals for assuring the quality of these complex systems are present in the literature. In this paper, we extended and improved our previous approach for semi-automated model-based generation of executable test scripts. Our proposal is oriented to system-level acceptance testing of IoT systems. We have implemented a prototype tool taking in input a UML model of the system under test and some additional artefacts, and producing in output a test suite that checks if the system’s behaviour is compliant with such a model. We empirically evaluated our tool employing two IoT systems: a mobile health IoT system for diabetic patients and a smart park management system part of a smart city project. Both systems involve sensors or actuators, smartphones, and a remote cloud server. Results show that the test suites generated with our tool have been able to kill 91% of the overall 260 generated mutants (i.e. artificial bugged versions of the two considered systems). Moreover, the optimisation introduced in this novel version of our prototype, based on a minimisation post-processing step, allowed to reduce the time required for executing the entire test suites (about -20/25%) with no adverse effect on the bug-detection capability.
ISSN:0963-9314
1573-1367
DOI:10.1007/s11219-021-09565-y