The role of Artificial Intelligence in Quality Assurance

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The role of Artificial Intelligence in Quality
Assurance
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Introduction Software testing efficiency
and software testing effectiveness are two key
metrics that determine the overall progress of a
test strategy. Artificial Intelligence (AI) and
Machine Learning (ML) in testing essentially
focus on these two parameters. AI ML can
optimize risk coverage, prevent redundancies,
perform portfolio inspection, detect false
positives, diagnose defects automatically, and
analyse user experience.    It is estimated that
more than 60 of the test cases in an enterprise
test case portfolio are redundant. AI identifies
such test cases that are physically as well as
logically identical and eliminates the
duplicates, which do not add any business value
and can be removed without decreasing the
business risk coverage. AI is capable of
maximizing defect detection and risk coverage
while minimizing costs, execution time,
and the number of test cases by identifying the
optimal test sets. It can uncover weak spots in
test case portfolios by tracking flaky test
cases, unused test cases, untested requirements,
and those test cases that are not linked to the
requirements. Additionally, AI has self-healing
automation properties, which means it can heal
the broken automated test cases and make test
automation better resilient to changes. 
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The present state of AI-driven software
testing  AI has been buzzing around
since the 1900s and it still upholds the hype
across the globe. Everyone keeps talking about
the possibilities of the role of AI. However,
there is still a wide gap between where AI has
reached today and where it has to go. Kevin
explains the present state of AI in software
testing as, The vision, the hope for everybody
is that someday, AI will be able to do the
testing for us. Were not there yet. Im not
promoting that. But what is definitely here is
AI-based tools and AI that helps us with our
jobs. So, we shouldnt look at it as AI replacing
testers yet, we shouldnt look at it as AI
replacing really most of our processes yet. What
AI does right now is it helps us be better
testers, meaning it takes out some of that
mundane work that we wouldnt like to do anyway.
Or maybe as well hear a little bit later, AI can
help us do things like prediction or analytics
better than weve done in the past, which just
allows us to do our jobs better. 

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AI resolving the Test Automation
trap  Software testing is a time-consuming and
cost-intensive activity. A challenge with
regular test automation is that by the time the
test code is completed, the requirements start
changing and applications start evolving with
regards to business functionality and UI. This
means that the whole effort put into developing
the test code goes into vain and you need to
adapt the test automation needs accordingly.
Kalyan calls it the Test Automation trap. He
explains, Test automation trap is when the test
teams are not getting enough time to be able to
do the failure triage from the previous test run
before building the next test automation
code. That is where AI can be really used to
solve this dilemma and to accelerate the manual
testing. With some of our clients, we are able to
apply AI in the context of prioritizing test
cases and also maintaining the test automation
code in an automated manner, as opposed to
manually investigating what needs to be changed.
And I expect that over a period of
time, well see that it can play a great role in
the context of analyzing the test results and
also deciding on what needs to be tested and
things like that, which can happen freely without
human intervention.  Read Full Blog at
https//www.cigniti.com/blog/artificial-intelligen
ce-qa-testing/
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