Machine learning is already being employed in millions of applications around the world—and it’s already starting to shape how we live and work, often in ways that go unseen.
Some of the “sexier” applications of machine learning are in emerging technologies like self-driving cars; thanks to ML, automated driving software can not only self-improve through millions of simulations, it can also adapt on the fly if faced with new circumstances while driving.
So how exactly does machine learning affect the world of software development and testing, and what does the future of these interactions look like? Even in today’s best software testing environments, machine learning aids in batch processing bundled code-sets, allowing for testing and resolving issues with large data without the need to decouple, except in instances when errors occur.
And, even when errors do occur, the structured ML will alert the user who can mark the issue for future machine or human amendments and continue its automated testing processes.
Already, ML-based software testing is improving consistency, reducing errors, saving time, and all the while, lowering costs.
As it becomes more advanced, it’s going to reshape the field of software testing in new and even more innovative ways.
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