The current growth of AI and ML augments tester’s 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. Read more: https://www.cigniti.com/blog/ai-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 tester’s 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 Tester’s 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
Cigniti’s 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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