What can machine learning do for testing? - Software Testing News

With the move to DevOps and high-paced development, there is a greater and more frequent need to specify test environments to ensure that systems are working efficiently; yet the ability of enterprise to model and manage capacity accurately is immature. Performance testers are theoretically well-placed to help but they may be naturally cautious about modelling capacity since testing functions can run up significant annual costs in capacity usage alone. You'll have heard plenty about AI (artificial intelligence) and ML (machine learning) of late, and with good reason – delicate, complex and downright costly technology and tools are rapidly maturing into usable toolsets in a wide range of verticals. Analyst firms predict huge markets for AI and ML, indeed the number of enterprises implementing artificial intelligence (AI) grew 270 percent in the past four years and tripled in the past year, according to industry analyst Gartner's 2019 CIO Survey. Results showed that organisations across all industries use AI in a variety of applications but on the downside struggle with acute talent shortages.