With software development life-cycles becoming more complicated by the day and delivery time spans reducing, testers need to impart feedback and evaluations instantly to the development teams. Given the breakneck pace of new software and product launches, there is no other choice than to test smarter and not harder in this day and age.

With this becoming the new normal, we notice that releases that happened once a month, now occur on a weekly basis and updates are inducted in on almost every alternate day. Thus, it is quite evident that the key to streamlining software testing and making it more smarter/efficient is Artificial Intelligence.

Down the line, Artificial Intelligence will be able to observe users performing exploratory testing within the testing site, using the human brain to assess and identify the applications that are being tested. In turn, this will bring business users into testing and customers will be able to automate test cases fully.

When user behavior is being assessed, a risk preference can be assigned, monitored and categorized accordingly. This data is a classic case for automated testing to evaluate and weed out different anomalies. Heat maps will assist in identifying bottlenecks in the process and help determine which tests you need to conduct. By automating redundant test cases and manual tests, testers can, in turn, focus more on making data-driven connections and decisions.

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