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 objective is to equip QA groups with the required insights to swiftly determine and resolve points, guaranteeing larger software program high quality and improved efficiency. These options will revolutionise how our customers method check automation.”
Key options embody:
AI copilot dashboard simplifies knowledge evaluation by permitting customers to simply work together with knowledge utilizing pure language queries and obtain actionable insights. It presents 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 cut back check execution time and enhance software program high quality considerably with lowering 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 improve 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 knowledge, customers can determine efficiency bottlenecks, troubleshoot check failures successfully and proactively tackle potential issues for every session run.
Check 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 determine continuously failing checks.
Attract check insights with HyperExecute supplies a time-series evaluation of check execution outcomes utilizing Attract reviews. Customers can monitor check standing, length 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 developments.
For extra details about LambdaTest’s newest options and to begin utilizing these highly effective analytics instruments, go to https://www.lambdatest.com/.
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