Page - 88 - in The Future of Software Quality Assurance

88 R. Marselis embodiment of a robot, but also the placement of a robot in the world in which it is located. 3 TestingWith IntelligentMachines The goal of using machine intelligence in testing is not to take people out of the loop. The goal is to make testing easier and faster, just like with traditional use of tooling to support any activity. Also, some tasks that couldn’t be done before are now possible by using intelligent machines. So, it is about enablement, effectivity, andefficiency. The future sees test engineers as quality forecasters that use smart sets of test caseswhichevolveto thebestfitofaddressingproblemareasforasmartsolutionin therightvarietyofsituations.Quality forecastingisaimedatbeingaheadof the test results, to make sure that quality problems are addressed even before any failure occurs for the user. To get to that situation, many preconditions must be fulfilled. Data about changes and tests thereof must be gathered in a structured way, models areused todescribe thesystem,AImust beused toanalyze thisdata. Digital test engineering evolves over time. Forecasting technology coming our way helps us to be ready to find the right tools, roles, and skills that keep guarding thequalityaskedof futureproducts. In the digital age, new technology is extending human possibilities, new ways of working, new thoughts, and takes on existing products. Where new things are created, things are tried and tested. This also applies to using intelligent machines for testing.Thecommondenominatorswith alldigital terminologyare: • Speed:Extremely fast market response. • Data: Hugeamountsofdataarecollected. • Integration:Everyoneneeds to integratewitheverything. Extremelyhighspeed,hugeamountsofdata, andan infiniteamountof possibil- ities for integrationare the elements we are facing for testing. They extendbeyond ourhumancapabilities. One way to help us out is test automation. Test automation in the context of digital testing, is automating everythingpossible in order to speed up the complete product development cycle using all means possible; even a physical robot taking overhumantest activities, suchaspushingbuttons, isapossibility.Furtherhelpcan be found in combining it with another new technologynot yet mentioned:artificial intelligence (AI). AI works with huge amounts of data, finds smart ways through infinite possibilities, and has the potential to hand us solutions quickly. Let us not forget that AIneeds tobe tested aswell, butAI canmake the differencehere. Technologies like artificial intelligence can help us in testing, for example, IoT solutions.Specifically,anevolutionaryalgorithmcanbeused togenerate test cases. Together with the use of multimodel predictions for test environments, test cases becomesmart test cases (Fig.3).