How is QA boosting its performance with AI adoption? (1)

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How is QA boosting its performance
with AI adoption?
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How is QA boosting its performance with AI
adoption?
An interesting and futuristic piece on Artificial
Intelligence (AI) has stated, because these AI
systems dont actually comprehend the underlying
logic of what they do, teaching them to do
anything else, even if its pretty similar
like, say, recognizing specific emotions  means
training them all over again from scratch. Once
an algorithm is trained, its done, we cant
update it anymore. Scientists and researchers
are working relentlessly to improvise the
performance of AI and take it to the next level
to facilitate various businesses. The article
further mentions, For years, scientists have
been trying to figure out how to work around the
problem. If they succeed, AI systems would be
able to learn from a new set of training data
without overwriting most of what they already
knew in the process. Along with multiple strides
in other business zones, AI has been a major
enabler in the software development cycle.
Especially, with software development and testing
getting complicated and the delivery time getting
shorter, AI is expected to bring substantial
value for development teams. Foresight for
Software Development and Testing with
AI Software applications and systems are being
launched at breakneck speed to ensure that the
brand and the business sustains in the competing
market scenario. Hence, the need to develop
faster and test smarter grows each day. Releases
are expected every week and updates can happen
even more frequently in such a scenario.
Ultimately, a lot depends on how AI evolves to
support Software Testing.
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How is QA boosting its performance with AI
adoption?
Referring to the contributions by AI to the work
process and within the working scenario, Svetlana
Sicular, Research Vice President at Gartner
states, Many significant innovations in the past
have been associated with a transition period of
temporary job loss, followed by recovery, then
business transformation and AI will likely follow
this route. AI will improve the productivity of
many jobs, eliminating millions of middle- and
low-level positions, but also creating millions
more new positions of highly skilled, management
and even the entry-level and low-skilled
variety. The role and contribution of AI is
still being defined and continues to emerge.
Nevertheless, when it comes to testing, AI gives
the added scope for testers to go beyond the
traditional mode of testing and adopt AI-enabled
automation platforms more rigorously and with
much more ease. Mike Rollings, Research Vice
President at Gartner, says, AI can take on
repetitive and mundane tasks, freeing up humans
for other activities, but the symbiosis of humans
with AI will be more nuanced and will require
reinvestment and reinvention instead of simply
automating existing practices. Rather than have a
machine replicating the steps that a human
performs to reach a particular judgment, the
entire decision process can be refactored to use
the relative strengths and weaknesses of both
machine and human to maximize value generation
and redistribute decision making to increase
agility. A practical approach will be when
AI-powered continuous testing platforms are able
to change controls as per the requirements and
constantly update the algorithms with any
miniscule indication or requirement. Especially,
with automation
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How is QA boosting its performance with AI
adoption?

testing, AI is being leveraged, where testers
pre-train controls and create a technical map to
identify the controls and label them. With
identification of frequently used controls,
testers can even create a hierarchy of the
controls to accelerate and streamline the testing
process. These are some expected results of
implementing AI within the testing cycle, but
what lies ahead is something that QA and Software
Testing needs from AI implementation. What QA
expects from AI Adoption? Practically, the
apparent expectation would be to reduce the
testing lifecycle, make it shorter, and smarter.
As per a recent article, Gartner believes strong
growth (for AI) will appear in the customer
experience sector while enterprise players
experiment with AI and offshoot technology, such
as deep learning, neural networking, and machine
learning software. It further mentions,
AI-based agents account for roughly 46 percent
of global AI-derived business value in 2018.
However, this is expected to slide to 26 percent
by 2022 as enterprise players invest further in
more sophisticated solutions offered by
AI. Read Full Blog at https//www.cigniti.com/
blog/quality-assurance-performance-with-ai-adoptio
n/
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