Thus, while the well-known testing method still forms the
foundation of software
testing, certain new trends are set to redefine the concept of software
testing in 2021. They include:
1. Agile and DevOps
Agile is a software development approach that emphasizes the
need for collaboration of teams, planning and sustainable learning that leads
to gradual deliveries. Therefore, to agile, software testing is an integral and
inevitable part of the SDLC.
DEVOPS seems to shorten SDLC and activate the end-to-end
process management by combining two main Vertical dev or development and
operation or operation. By removing the boundaries between development,
operation, and testing, it's:
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testing company
Increase the speed of spread
Cut downtime to the market
Promote better ROI
So when adopted, this process helps shorten SDLC and allows
the release of superior applications qualitatively. This is not relatively new,
but adopting these practices to adapt to the current time will definitely be a
trend testing software that is worth watching.
2. Qaops.
It has started the trend recently. It combines QA techniques
with operations or operations to ensure additional shipping without sacrificing
application quality. The essence of Qaops is related to integrating QA
techniques into CI / CD pipes to enable better communication and collaboration
between team members. The main benefits of this ideology include:
Superlative product development
Increased ability to meet deadlines
Fast addition new features etc.
The new thought process also sees the possibility of
integrating QAOP with Devops. This will effectively combine continuous testing
with CI / CD. The benefits produced will be an accelerated product-to-market
timeline scale and rapid error detection.
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3. AI and ML
AI and ML potential has not been fully realized. Therefore,
the trend of combining AI and ML to automate business processes will continue
in 2021. Statistical predictions when placing this popularity in absolute
conditions:
AI will grow on a CAGR of 36.6% from 21.5 billion dollars in
2018 to 190.6 billion dollars in 2026
ML Market is scheduled to grow on CAGR 44.1% from 1.03
billion dollars in 2016 to 8.81 billion dollars in 2022.
This growth is mainly because of them:
Facilitating the use of real-time data for better business
decision making
Increase the quality of automated
software testing by activating fast error detection etc.
Leveraging AI and ML allows the QA team to follow the
release of frequent applications and improve automatic software testing
strategies. By using the detection of redundant test cases, AI-powered test
application optimizes the suite test. In addition, keyword analysis in RTM
maximizes the coverage of the test.
At present, AI is being developed to be used as part of a
healing mechanism and visual testing present in automation test equipment. With
visual testing AI activated, fewer written tests needed for functional testing
the user interface.
The development of AI-powered self-healing mechanisms will
reduce costs incurred in the creation of automatic tests, reducing test
maintenance and effectively correcting test script problems quickly.
Smart automation is not possible without ML. While human
intelligence is still the main tool available to estimate consumer behavior
patterns, predictive analytics of ML applications that are activated can be
used to strengthen human intelligence by detecting areas that have not been
explored in the application.
For now, the use of ml in software testing is just an
interesting possibility. However, the future will carry out analytical-based
initiatives to get traction. This will further increase the identification of
the problematic area and make it under the scope of the test.
4. Test automation is not time
At present, various types of applications are being
developed and released with increasing speed. This means testers need to
continue to learn new and complicated programming languages whenever needed.
This is a time-consuming experience and frustrating.
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Read: software testing services company
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