Automation in software testing has existed for some time
now. With increasing expectations for faster releases and fast updates, manual
software testing no longer cuts it. Therefore, organizations shift towards
developing and testing automatic software.
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Seeing the process of testing the traditional automatic
software in the SDLC raises the awareness that it does not produce the desired
results with investment benefits that have been included in it. The main reason
for this emerged as an organization still followed the waterfall software
development methodology where software testing came in the end. After
recognizing the need to shift the QA process at the beginning of the software
development cycle, the organization began to embrace what is called quality
engineering.
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Software engineering is about shifting the focus of checking
quality in the end to ensure that the quality is built into the code while being
developed. Having software testing is running parallel with the development
process, with the help of automation, allowing organizations to get rid of
obstacles posed by the methodology of development software inheritance.
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Quality engineering practices driven by automation are
despite a relatively efficient way to accelerate speed to market and maintain
changing customer demand, there is still a large space for further improvement.
For example, to automate test cases, most of the time must be invested to
identify, prioritize, and write a test case. Therefore, the need for more
efficient and faster ways to implement automation appear, which brings
artificial intelligence and engine learning into the image.
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Introducing cognitive capabilities into quality engineering
By keeping the views that look forward, we can hope to have
a complete range of cognitive computing that crosses in a quality engineering
life cycle, which will include technology such as deep learning, self-healing,
and natural language processing, in addition to intelligence and artificial
machine learning.
The introduction of AI into quality engineering allows the
process of automation to carry out heavy lifts related to overall test
management, while manual professionals get bandwidth to explore creative
methods to improve the final quality.
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The current market dynamics have required the implementation
of the Association of Agile + Devop's approach to SDLC. While agile carrying
the required speed, Devop promotes a culture of collaboration and eliminating
silos between departments. CI / CD pipes established with these methodologies
help streamline and accelerate the development and release process. However,
there are often formal metric shortcomings to measure performance and release
functions.
The quality engineering driven AI and ML can produce
optimization and acceleration of application quality and delivery speed, while
keeping the proper KPI trail and metrics that need to be measured.
Smart ability, cognitive AI and ML algorithm allows
organizations to take deformed prediction approach rather than defective recipe
approach. This means, with the algorithm time can predict areas where defects
can occur and allow developers to fix them proactively. Take predictions,
instead of prescriptive approaches, saving considerable time in the SDLC as a
whole by reducing the need for constant back and forth between Dev and QA for
detection of defects and repairing defects.
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Furthermore, AI and ML algorithms can be used to automate
the functional and non-functional aspects of software testing together with the
test data environment and test suite optimization.
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