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.
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
End-Goal of Software Testing
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:
What is the scope of testing?
What bugs need to be targeted?
What are user behaviors likely to occur?
How should test cases be designed?
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.
Let’s look at some of the specific ways AI and ML can streamline the testing process while also making it more robust.
Save Time in Testing
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.
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
Improved Regression Testing
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
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.
Selecting the Appropriate Tests
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 Process Automation
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
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
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.
Determining the Root Causes for Failures
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.
AI/ML Into Testing Software
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.
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.