How do we foresee the future of Test Automation?

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How do we foresee the future of Test
Automation?
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How do we foresee the future of Test Automation?
Test Automation is like a self-healing process
for Software Testing and Quality Assurance. When
software glitches keep mushrooming constantly,
business risks grow and testing teams face major
challenges. Automation frameworks enable testing
and development teams to enhance the testing
activities and accelerate the development tasks.
According to a report released by SBWire, The
global test automation market will likely expand
at a robust CAGR of 15.4 from 2017 to 2025 to
become worth US 109.69 bn by 2025 from US 30.45
bn in 2016. With Artificial Intelligence and
Machine Learning getting implemented across
applications and products, testing needs to get
much more result-oriented. Automation frameworks
help teams to customize their testing activities
as per the requirements of the project. Most
importantly, the tools and platforms help to
backtrack the activities and accordingly take
decisions for implementing automation and
building test cases as well. As the development
scene evolves and gets more complicated, test
automation and automation platforms will have to
twist their ways and ensure competency. It will
be interesting to know the kind of future that
test automation can expect in the light of the
changing development scenarios. Increasing
relevance and application of AI and ML The
challenges surrounding testing and development
are expected to only increase over a period of
time. This implies that testing and development
teams will have to induce more measurability and
traceability within the QA processes. Hence,
testing will have to leverage ML and AI to bring
in more efficiency than an automation framework
or a manual testing process.
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How do we foresee the future of Test Automation?
Additionally, the testing methodologies will have
to build predictable models to ensure seamless
Continuous Integration and Continuous Deployment
through the development cycle. Eventually, the
focus will be on delivering experiences as
against mere outcomes. Thats where the role of
Testing and QA will transform substantially. AI
and ML will become a part of the solution and not
just a tool to support the process. Manage large
amount of data Data forms a significant
component in the software development and testing
process. This data can take multiple formats and
ultimately the type of data will more or less
determine how the testing cycle will proceed and
contribute to the overall application
development. The kind of data collected will also
help to improve testing and bring in added
efficiency. With methodologies such as DevOps
and Agile getting implemented, data becomes
highly critical for teams to build strong test
cases that can contribute even in the longer run.
Hence, there will be a growing need for testing
to process large chunks of data and incorporate
it within the testing gamut. This will also make
the testing process more interactive and
responsive for constant evolution. Incorporating
Smart Analysis and Smart Reporting structures As
we have discussed, data is critical for every
testing process, as it helps to bring in
effective analysis and highlight any potential
historical vulnerabilities. For instance, during
a security testing scenario, any testing group
will need past analysis
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How do we foresee the future of Test Automation?
that will enable them to take smart decisions for
better outcome. In this way, the developers will
be equipped with accurate vulnerability data
almost in real-time. This means that testing
teams will have to incorporate structures and
platforms that contribute towards smarter
analysis and effective reporting of
data. Implementing AI for better Code
quality What you need ultimately is a code that
ensures a quality application and is free from
vulnerabilities. This is possible by implementing
AI-enabled platforms that ensure effective
validation across various parameters. Aspects
such as smart reporting and smart analysis can be
achieved by incorporating AI within the
automation test results. This will also help to
spot the bugs better and much early in the
testing and development cycle. The best option
would be to bring in a hybrid model that
incorporates both automated, manual, or
AI-enabled testing platforms that are applicable
for the concerned requirements. AI and ML can
come in wherever needed, rather than just being
applied for the sake of it. Automation can work
better with smart data that gets reported
effectively and analyzed smartly. It will be help
to assure code quality even in the longer run by
ensuring cost effectiveness as well. Read Full
Blog at https//www.cigniti.com/blog/foresee-the
-future-of-test-automation/

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