LambdaTest has introduced the launch of its analytics options empowering QA groups with deeper insights and enhanced management over check automation. The brand new options use AI and ML to optimise software program high quality and efficiency.
“We’re thrilled to introduce these cutting-edge analytics instruments to our platform,” mentioned Mayank Bhola, the co-Founder and head of product at LambdaTest. “Our aim is to equip QA groups with the mandatory insights to swiftly establish and resolve points, guaranteeing greater software program high quality and improved efficiency. These options will revolutionise how our customers method check automation.”
Key options embrace:
AI copilot dashboard simplifies information evaluation by permitting customers to simply work together with information utilizing pure language queries and obtain actionable insights. It provides predictive analytics and clever suggestions, serving to groups make data-driven choices effectively.
AI-powered flaky check analytics shares invaluable insights into check suite behaviour, enabling groups to scale back check execution time and enhance software program high quality considerably with reducing the discharge cycle time. By figuring out and prioritising flaky checks primarily based on their affect, groups can optimise debugging efforts, speed up testing cycles and enhance check reliability.
LambdaTest’s command logs analytics supplies granular insights into check execution, enabling QA groups to pinpoint points and optimise check scripts with out stale components. By analysing command-level information, customers can establish efficiency bottlenecks, troubleshoot check failures successfully and proactively handle potential issues for every session run.
Take a look at case insights simplifies the evaluation of check automation execution on LambdaTest at every step of the check session. These insights assist in check case well being evaluation, displaying success versus failure charges and analysing check instances by group to establish often failing checks.
Attract check insights with HyperExecute supplies a time-series evaluation of check execution outcomes utilizing Attract reviews. Customers can monitor check standing, period and suite particulars, assess suite well being, analyse check standing ratios and consider the common check durations of the check suites with a number of customized filter choices.
These options can be found globally to all LambdaTest customers, addressing frequent check automation challenges and offering detailed insights into check instances and execution traits.
For extra details about LambdaTest’s newest options and to start out utilizing these highly effective analytics instruments, go to https://www.lambdatest.com/.
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