Augment Human Testers First in the Path to AI-Based Autonomous Testing

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Augment Human Testers First in the Path to
AI-Based Autonomous Testing
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Augment Human Testers First in the Path to
AI-Based Autonomous Testing
Software development focuses on innovation, and
existing software is modernized to cope, while
continuous delivery means that both modernized
and new software are deployed more regularly. How
can testers handle more frequent testing while
preserving or improving quality? They need to
figure out how to help development teams provide
high-quality work quickly. Yes, test automation
must improve, but in order to do so, the testers
practice intelligence must improve as well. If
testers have been irritated by simple (but rigid)
siloed application testing, they will become even
more frustrated when applications and
infrastructure designs grow more dispersed and
multilayered, with hundreds of APIs and
microservices. Past and existing testing methods
are incapable of dealing with this expanding
complexity add speed to the equation, and the
situation becomes considerably worse. Augment
Testers Intelligence so they can test more
effectively The successful use of information
technology to supplement human capabilities is
referred to as augmentation. The current growth
of AI and ML augments testers intellect by
allowing them to swiftly access a variety of data
and make better-informed decisions, as well as
assist them in optimizing test techniques,
selecting increased automation, and more. Testers
will be augmented by Provide robust APIs for
business to test Using testing technologies to
augment business testers implies allowing them to
accomplish more of what technical testers do
(e.g., automate APIs, test in a more complex
context, and test more precisely).
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Augment Human Testers First in the Path to
AI-Based Autonomous Testing
Predict failures in the future Software testers
can use machine learning predictive models to
identify possible issues and prevent them from
occurring. Predictive analytics models are
already being used by a number of companies to
assist, predict, and avert production
mishaps. Identify and fix UI bugs on the web and
on mobile devices Automation testing based on
user interfaces has existed for a long time, but
it has never been precise or powerful enough.
However, several new disruptive businesses are
using artificial intelligence and machine
learning to scan web app and mobile UIs in order
to find simple flaws and solve them. Optimize
test data to reduce the time it takes for
automation to run If the testing data is
incorrect, testing techniques cannot ensure
quality. This use case assists testers in
determining the best set of test data and any
necessary changes, minimizing the amount of
possible combinations and testing hours. There
are non-ML-based combinatorial algorithms that
can do this, but ML and DL are more precise.
Synthetic data that mimics the real data model,
data transactions, and changes in production is
also created using an AI and ML-based technique,
which aids testing. With effective defect
management, you can cut your mean time to repair
These tools or services assist dev teams in
determining the commonalities and clustering of
bug issues, gaining insight into the types of
fixes that are required, or identifying a code
area that need repair. Developers can also use
this use case to crunch petabytes of data from
previous projects.

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Augment Human Testers First in the Path to
AI-Based Autonomous Testing

Cignitis extensive AI, machine learning, and
analytics expertise assists businesses in
improving their automation frameworks and quality
assurance processes. Cigniti delivers
AI/ML-driven testing and performance engineering
services for your QA framework using its
next-generation IP, BlueSwanTM. Cigniti has
established a 4-pronged AI-led testing approach,
with a strong focus on AI algorithms for test
suite optimization, defect analytics, customer
sentiment analytics, scenario traceability,
integrated requirements traceability matrix
(RTM), rapid impact analysis, comprehensive
documentation, and log analytics. Consult our
team of AI Testing specialists to learn more
about augmenting human testers first in the path
to AI-based autonomous testing. Read more
https//www.cigniti.com/blog/ai-autonomous-testing

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