Augmented testing: Industry feedback to shape a new testing technology
Paper i proceeding, 2019

Manual testing is the most commonly used approach in the industry today for acceptance-and system-testing of software applications. Test automation has been suggested to address drawbacks with manual testing but both test automation and manual testing have several challenges that limit their return of investment for system-and acceptance-test automation. Hence, there is still an industrial need for another approach to testing that can mitigate the challenges associated with system-and acceptance-testing and make it more efficient and cost effective for the industry. In this paper we present a novel technique we refer to as Augmented Testing (AT). AT is defined as testing through a visual layer between the tester and the System Under Test (SUT) that superimposes information on top of the GUI. We created a prototype for AT and performed an industrial workshop study with 10 software developers to get their perceived benefits and drawbacks of AT. The benefits and drawbacks will be useful for further development of the technique and prototype for AT. The workshop study identified more benefits than drawbacks with AT. Two of the identified benefits were: 'Know what to test and what has been tested' and 'Less manual work'. Due to these results, we believe that AT is a promising technique that deserves more research since it may provide industry with new benefits that current techniques lack.

Test Automation

System Testing

Industrial Workshop Study

Augmented Testing


Michel Nass

Blekinge Tekniska Högskola, BTH

Emil Alégroth

Blekinge Tekniska Högskola, BTH

Robert Feldt

Chalmers, Data- och informationsteknik, Software Engineering

Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019

176-183 8728937

12th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019
Xi'an, China,



Inbäddad systemteknik

Datavetenskap (datalogi)



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