By Erik Fogg | November 30, 2020

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- and mid-sized could benefit from testing software using AI/ML to meet some of the challenges faced by QA teams. While AI and ML are not substitutes for human testing, 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 testing plans should incorporate all of these to improve deployment success. Even while facing time constraints and ever-changing requirements, testing cycles are increasingly quick and short. Yet, they still demand high quality in order to meet 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 software testing reduces the time spent on manually testing. Teams are then able to apply their efforts to more complex tasks that require human interpretation.

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

With more rapid deployment, there is an increased need for regression testing, to the point where humans cannot realistically keep up. Companies can use AI for some of the more tedious regression testing tasks, where ML can be used to generate test scripts.

In the example of 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 of the changes that a QA tester may miss.

When introducing a change, how many tests are needed to pass QA and validate that there are no issues? Leveraging ML can determine how many tests to run based on code changes and the outcomes of past changes and tests.

ML can also select the appropriate tests to run by identifying the particular subset of scenarios affected and the likelihood of failure. This creates more targeted testing.

With changes that may impact a large number of fields, AI/ML automate the validation of these fields. For example, a scenario might be Every field that is a percentage should display two decimals. Rather than manually checking each field, this can be automated.

ML can adapt to minor code changes so that the code can self-correct or self-heal over time. This is something that could otherwise take hours for a human to fix and re-test.

While QA testers are good at finding and addressing complex problems and proving out test scenarios, they are still human. Errors can occur in testing, especially from burnout syndrome of completing tedious processing. AI is not affected by the number of repeat tests and therefore yields more accurate and reliable results.

Software development teams are also ultimately composed of people, and therefore personalities. Friction can occur between developers and QA analysts, particularly under time constraints or the outcomes found during testing. AI/ML can remove those human interactions that may cause holdups in the testing process by providing objective results.

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

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

While the human element will still exist, introducing testing software that incorporates AI/ML will overall improve the QA testing within an organization. Equally as important 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 the outcome are not well suited for AI and ML.

But for understanding user behavior, gathering data analytics will build the appropriate test cases. This information identifies the failures that are most likely 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