Utilizing Smart Prediction of Vulnerable Areas in Testing With Machine Learning
Modern-day testing has undergone a paradigm shift driven by the dynamic nature of software development practices. In this white paper, we delve into this paradigm shift and look at how machine learning algorithms can be utilized for greater quality engineering success.
by Rajesh Natarajan, Delivery Team Lead
Apr 08, 2024
1 min read
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Modern-day testing is characterized by a blend of traditional manual testing and the pivotal role of automation testing, both of which play a big part in quality engineering. There are often many challenges faced by testing teams in contemporary environments, but with the arrival of the use of machine learning (ML) algorithms in automation testing, anticipating vulnerable areas has become more important than ever.
This is where machine learning can come into its own as an integral part of the testing process. Automation helps with faster testing, with machine learning predicting what will fail before testing even begins, which will ensure that quality is embedded into development at the very start of the testing process.
Key takeaways:
The usage of machine learning (ML) algorithms in quality engineering today.
The differences between modern-day testing approaches
The use of automation in quality engineering as a new approach.
Utilizing machine learning algorithms for greater testing success.
