Involvement in testing

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Artificial Intelligence started in the 1950s, picked up pace steadily, braved the AI winters, and now, it is omnipresent in different fields like defense, medicine, engineering, software development, data analytics, etc.

Technology

Involvement InTesting
Artificial Intelligence started in the 1950s, picked up pace steadily, braved the AI winters,
and now, it is omnipresent in different fields like defense, medicine, engineering,
software development, data analytics, etc.

Where AI wins
▪ Test case generation:Test case generation with AI saves a significant amount of time and effort.
It also renders scalability to software testing.
▪ Test data generation: AI can generate a large volume of test data based on the past trends within
a matter of seconds, which otherwise can take more time if left for manual work.
▪ Test case maintenance: AI can dynamically understand the changes made to the application and
modify the testing scope accordingly.
▪ Predictive analysis: AI certainly has an advantage when it comes to analyzing a huge amount of
test results in a short time. It can scan, analyze and share the results along with the recommended
course of action with precision.
▪ We have a detailed blog that covers the benefits of AI testing and intelligent automation.
Click here to read more.

Where humans are still needed
Edge test cases:There might be certain test scenarios where a judgment call needs to be taken. IfAI does not have enough
data and learnings from the past, it may falter.That is when human intervention is critical.
Complex unit test cases: Unit testing for complex business logic can be tricky. AI can simply generate a unit test case based
on the code it has been fed. It cannot understand the intended functionality of the module. So if there is a flaw in the

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Testing of AI will require a new skillset related to interpreting a system’s boundaries or tolerances. Indeed, as our paper points out, the complex functioning of an AI system means, amongst other things, that the focus of testing shifts from output to input to verify a robust solution. Also we introduce the 6 angles of quality for Artificial Intelligence and Robotics. This paper was written by Humayun Shaukat, Toni Gansel and Rik Marselis.

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