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QA Financial, UK: Cognizant’s Vice President of Quality Engineering & Assurance Says Artificial Intelligence can Help Companies Reduce Risk and Keep That Edge in Time-to-Market

“If AI (artificial intelligence) is going to lead to a paradigm shift in the way software is tested, that shift is still some time away,” says Andreas Golze. “The very nature of intelligent systems means that there is an element of learning — the system will make mistakes that need to be corrected so that it will not repeat them. It is hard to say how long it will take before they are really effective.” Excerpts:

“If we imagine the progress of AI technology as a curve, we do not know where we are. We might be at the period where the technology rapidly accelerates in sophistication and usefulness, or we might be a few years or more from that.

It is still early days for practical applications of the technique and experts are divided on how mature the technology is.

There is a lot of hype currently associated with the term AI. It is not a revolution, it is a new name for things we have used before.

You never have the time to scan the whole ocean, but defects come like fish, in groups. There are some areas where there are many, and some where there are none.

Past test data, fed into a machine-learning system, will then predict what the most at-risk components of the application are.

You never have the time to test everything, and AI can help you reduce risk while keeping that edge in time-to-market.

This requires comprehensive documentation of interdependencies between software components because if it is missing, then the machine-learning program cannot accurately predict what areas of an application will be impacted by any change.

For many older applications the documentation is missing, or not up to scratch, it might be the case then that AI is useful for test-strategy but only for newer applications that are built with these requirements in mind.”

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