AI IN TESTING AND AUGMENTED REALITY
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McKinnel (2019) mentions that AI-efficient constant testing platform is able to identify the
various controls that face changes. This is done more efficiently than the human beings (McKinnel
2019). Here, constant updates are there to the algorithms even with minute modifications that are
witnessed (McKinnel 2019). As the automation testing is concerned, AI is useful broadly within the
categorization of object application for every kind of user interfaces (McKinnel 2019). In this place
the recognized measures are put into category as the testers and tools are created can pre-train
various controls see outside the setups (McKinnel 2019). As the control hierarchy is seen, the testers
are able to generate the technical map (McKinnel 2019). Thus the AI can look at the GUI to retrieve
the various control labels (McKinnel 2019). Having testing, the verification of outcomes, one
requires to access to the extra test data (McKinnel 2019). For instance, Li and (2017) states the
following,
“Google DeepMind has generated the program of AI using in–depth learning of
reinforcement for playing the video games from own. In this was lots of test data are produced.” (Li
and Du 2017)
Fehlmann (2019) explains that the AI, down the line can observe the users to perform the
exploratory type of testing under the testing site. This is done through utilizing the human brain for
determining and analyzing the applications tested (Fehlmann 2019) (Fehlmann 2019). This in turn,
would bring the users into testing (Fehlmann 2019). Thus the customers can automate the test cases
totally (Fehlmann 2019). As the behavior of user is analyzed, the preference risk is assigned,
monitored and then categorized as per the needs (Fehlmann 2019). Here, the information can be
regarded as the classic case for the automated testing for examining and eradicate various kinds of
anomalies (Fehlmann 2019). To determine the bottlenecks, heat maps cam be used in the process
(Fehlmann 2019). This also helps to find out which tests are required to be conducted (Fehlmann
2019). Furthermore, through automating the manual tests and redundant test cases, in turn, the test

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