An acceptance testing approach for Internet of Things systems. Issue 5 (1st October 2018)

• Record Type: Journal Article
• Authors: Leotta, Maurizio; Clerissi, Diego; Olianas, Dario; Ricca, Filippo; Ancona, Davide; Delzanno, Giorgio; Franceschini, Luca; Ribaudo, Marina
• Is Part Of: IET software. Volume 12:Issue 5(2018)
• Page Start: 430
• Page End: 436
• Publication Date: 2018-10-01
• Subjects: cloud computing -- Internet of Things -- program testing -- graphical user interfaces; acceptance testing approach -- things systems -- IoT systems -- graphical user interfaces -- black box testing -- test scenarios -- test cases -- corresponding test scripts -- mobile health IoT system -- test suites -- original m-health system; Computer software -- Periodicals; Software engineering -- Periodicals; 005.1
• DOI: 10.1049/iet-sen.2017.0344
• Languages: English
• ISSNs: 1751-8806

Abstract

Abstract : Internet of things (IoT) systems are becoming ubiquitous and assuring their quality is fundamental. Unfortunately, a few proposals for testing these complex, and often safety‐critical, systems are present in the literature. The authors propose an approach for acceptance testing of IoT systems adopting graphical user interfaces as a principal way of interaction. Acceptance testing is a type of black box testing based on test scenarios, i.e. sequences of steps/actions performed by the user or the system. In their approach, test scenarios are derived from a state machine that expresses the behaviour of the system under test, and test cases are derived from them by specifying the actual data and assertions and made executable by implementing the corresponding test scripts. As a case study, they selected a mobile health IoT system for diabetes management composed of local sensors/actuators, smartphones, and a remote cloud‐based system. The effectiveness of the approach has been evaluated by measuring the capability of two test suites implemented using different localisation strategies (visual and structure‐based) in detecting mutants of the original m‐health system. Results show the effectiveness of the test suites implemented by following the proposed approach since 93% of the generated mutants have been detected.