QA Increasingly Benefits from AI and Machine Learning – RTInsights

While the human element will still exist, incorporating AI/ML will improve the QA testing within an organization.

The needle in quality assurance (QA) testing is moving in the direction of increased use of artificial intelligence (AI) and machine learning (ML). However, the integration of AI/ML in the testing process is not across the board. The adoption of advanced technologies still tends to be skewed towards large companies.

Some companies have held back, waiting to see if AI met the initial hype as being a disruptor in various industries. However, the growing consensus is that the use of AI benefits the organizations that have implemented it and improves efficiencies.

Small- andmid-sized could benefit from testing software using AI/ML to meet some of thechallenges faced by QA teams. While AI and ML are not substitutes for humantesting, they can be a supplement to the testing methodology.

See also: Real-time Applications and Business Transformation

As development is completed and moves to the testing stage of the system development life cycle, QA teams must prove that end-users can use the application as intended and without issue. Part of end-to-end (E2E) testing includes identifying the following:

E2E testingplans should incorporate all of these to improve deployment success. Even whilefacing time constraints and ever-changing requirements, testing cycles areincreasingly quick and short. Yet, they still demand high quality in order tomeet end-user needs.

Lets look at some of the specific ways AI and ML can streamline the testing process while also making it more robust.

AI in softwaretesting reduces the time spent on manually testing. Teams are then able toapply their efforts to more complex tasks that require human interpretation.

Developers andQA staff will need to apply less effort in designing, prioritizing, writing,and maintaining E2E tests. This will expedite timelines for delivery and freeup resources to work on developing new products rather than testing a newrelease.

With more rapiddeployment, there is an increased need for regression testing, to the pointwhere humans cannot realistically keep up. Companies can use AI for some of themore tedious regression testing tasks, where ML can be used to generate testscripts.

In the exampleof a UI change, AI/ML can be used to scan for color, shape, size, or overlap.Where these would otherwise be manual tests, AI can be used for validation ofthe changes that a QA tester may miss.

Whenintroducing a change, how many tests are needed to pass QA and validate thatthere are no issues? Leveraging ML can determine how many tests to run based oncode changes and the outcomes of past changes and tests.

ML can alsoselect the appropriate tests to run by identifying the particular subset ofscenarios affected and the likelihood of failure. This creates more targetedtesting.

With changesthat may impact a large number of fields, AI/ML automate the validation ofthese fields. For example, a scenario might be Every field that is apercentage should display two decimals. Rather than manually checkingeach field, this can be automated.

ML can adapt tominor code changes so that the code can self-correct or self-healover time. This is something that could otherwise take hours for a human to fixand re-test.

While QAtesters are good at finding and addressing complex problems and proving outtest scenarios, they are still human. Errors can occur in testing, especiallyfrom burnout syndrome of completing tedious processing. AI is not affected bythe number of repeat tests and therefore yields more accurate and reliableresults.

Softwaredevelopment teams are also ultimately composed of people, and thereforepersonalities. Friction can occur between developers and QA analysts, particularlyunder time constraints or the outcomes found during testing. AI/ML can removethose human interactions that may cause holdups in the testing process byproviding objective results.

Often when afailure occurs during testing, the QA tester or developer will need todetermine the root cause. This can include parsing out the code to determinethe exact point of failure and resolving it from there.

In place ofgoing through thousands of lines of codes, AI will be able to sort through thelog files, scan the codes, and detect errors within seconds. This saves hoursof time and allows the developer to dive into the specific part of the code tofix the problem.

While the humanelement will still exist, introducing testing software that incorporates AI/MLwill overall improve the QA testing within an organization. Equally asimportant as knowing when to use AI and ML is knowing when not to use it.Specific scenario testing or applying human logic in a scenario to verify theoutcome are not well suited for AI and ML.

But forunderstanding user behavior, gathering data analytics will build theappropriate test cases. This information identifies the failures that are mostlikely to occur, which makes for better testing models.

AI/ML can also specify patterns over time, build test environments, and stabilize test scripts. All of these allow the organization to spend more time developing new product and less time testing.

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QA Increasingly Benefits from AI and Machine Learning - RTInsights