3.Understanding upcoming trends in test automation.pdf

12/28/22, 10:47 AM Understanding upcoming trends in test automation
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Understanding upcoming trends in test automation
by Shuvo A. | 0 comments
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How to understand the upcoming trends in test automation? The incorporation of automation has
revolutionized many industries and testing is no more an exception to this. The testing industry has
taken a long leap in the way forward as manual methods of testing have been replaced with test
automation. The incorporation of automation has replaced some of the repetitive tasks in regression
testing, helping QA teams to speed up project delivery cycles. Although automation has brought
significant change in the testing industry, there are still some bottlenecks – inadequate test coverage,
dependency on programmers, test prioritization, maintenance, and development of automation
scripts. In this article, we’ll discuss the upcoming trends in the test automation industry and how
testing will going to evolve from automation to autonomous.
Understanding upcoming trends in test automation [hide]
1 Upcoming trends in test automation
1.1 Incorporation of disruptive technologies – AI, ML, & NLP
1.2 Leveraging AI, ML, & NLP in Test Automation
Upcoming trends in test automation
Incorporation of disruptive technologies – AI, ML, & NLP
Artificial Intelligence (AI): AI refers to the ability of the bots/ machines to mimic a human behavior/
mind, such as learning and problem-solving.AI also allows machines to learn for themselves from
data and perform human tasks.

Machine Learning (ML): It is a subset of AI that enables machines to learn for themselves from
data. ML provides machines with the capability to learn from data and use this knowledge to evolve
themselves.
Natural Language Processing (NLP): It is a branch of artificial intelligence that is responsible for
allowing seamless interactions between machines and humans. It is used to make the interaction
between humans and machines as simple and close to natural language as possible.
Leveraging AI, ML, & NLP in Test Automation
AI, ML, & NLP can address various challenges associated with current test automation techniques.
Let’s take a look at how automated testing can be enhanced further with AI and other disruptive
techniques.
Test Creation& Script Design: Although enterprises have embraced agile and DevOps, software
testing is still a manual-driven process. The creation of test cases is still done manually and business
analysts, functional consultants, manual testers, and stakeholders participate in this to create user
stories. These non-technical users create test scenario descriptions while programmers/ technical
fellows create automation scripts, making it a time-consuming and costly process.
By incorporating NLP, this process can be streamlined. NLP allows business users to write test cases
in natural language i.e. English that does not need to be learned. Then, AI and ML eliminate the
dependency on programming professionals to create automation scripts. AL and ML-based engines
will generate automated scripts by reading English test cases from spreadsheets. Thus, time spent on
test creation is reduced significantly.

Test Script Maintenance: Traditional test automation frameworks use element locators to identify
controls on the screen. Whenever a change happens in the object property i.e. Name, ID, Xpath, or
CSS due to the introduction of new screens, buttons, user flows or a slight change in the UI, the
automation scripts break. A significant amount of time and effort is needed to maintain the test
scripts.
Consider a scenario of Oracle Cloud quarterly updates. In this, Oracle Cloud testing is required at
least 8 times a year. Imagine how much efforts and time is required by QA teams to maintain test
scripts as Oracle Cloud apps are highly dynamic in nature.
With self-healing capabilities, this problem can be addressed. Test automation frameworks that
leverage AI and ML can automatically identify the change made to an element locator (ID), or a
screen/flow. The self-healing engines are driven by AI and ML-based algorithms that dynamically fix
the test automation scripts without human intervention.
Test Recommendation: What to test in regression testing is still a challenge. Often, test engineers
are driven by their experience or guesses to pick up smoke/regression tests. This exposes your
business to serious risks and some critical test cases may be overlooked.
AI can solve this problem as it can precisely recommend the minimum number of tests that need to
be executed to keep business risks at bay.
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3.Understanding upcoming trends in test automation.pdf