With the advent of DevOps and Continuous Delivery, businesses are now looking for real-time risk assessment throughout the various stages of the software delivery cycle.
Although Artificial Intelligence (AI) is not really new as a concept, applying AI techniques to software testing has started to become a reality just the past couple years. Down the line, AI is bound to become part of our day-to-day quality engineering process, however, prior to that, let us take a look at how AI can help us achieve our quality objectives.
Day after day, QA Engineers face a plethora of difficulties and waste a lot of time to find a proper solution. When it comes to making new additions, the existing code which has already gone through the testing process may stop working.
Every time the development team expands on existing code, they must carry out new tests. While regression testing cycles can grab a long time, undertaking them on a manual basis is bound to overwhelm QAs.
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.
Releases that happened once a month, now occur on a weekly basis and updates are factored 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.
By assimilating machines which can meticulously mimic human behavior, the team of testers can move beyond the traditional route of manual testing models and progressively move forward towards an automated and precision-based continuous testing process.
An AI-powered continuous testing platform can recognise changed controls more efficiently than a human, and with constant updates to its algorithms, even the slightest changes can be observed.
When it comes to automation testing, Artificial Intelligence is being used widely in object application categorisation for all user interfaces. Here, recognised controls are categorised when you create tools and testers can pre-train controls that are commonly seen in out of the box setups. Once the hierarchy of controls is observed, testers can create a technical map such that the AI is looking at the Graphical User Interface (GUI) to obtain labels for the different controls.
With testing being all about verification of results, one needs access to a plethora of test data. Interestingly, Google DeepMind created an AI program that utilises deep reinforcement learning to play video games by itself, thus, producing quite a lot of test data.
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