LambdaTest, a unified enterprise test execution cloud platform, has launched AI-powered command logs analytics in its test intelligence platform. Customers can now intelligently analyze the test execution on the platform and gain insights into the error commands that are causing the test runs to fail.

First, the ‘command status summary’ widget offers a summary of the response statuses for Selenium commands executed on the platform. This is visualized using a tree map, giving a quick and clear overview of the system’s status. Next up, the ‘command error trends’ widget provides a time-series representation of command log HTTP response statuses and groups tests with similar response statuses for a clear view of the system’s health and performance trends. Users can also use the ‘command error categorization’ widget to sort unique error messages from command logs, allowing users to identify common issues and prioritize bug fixes.

Also, users can identify potential system bottlenecks and optimization opportunities with the ‘command type trends’ widget. It shows the trends of endpoint usage over time by displaying the count of commands for each name mapped to the endpoint/request path. Finally, users can see a time-series view of the number of tests where all commands were successfully executed with the ‘command success trends’ widget.

“Given the pace at which customers expect feature/product releases, it is important for digital businesses to keep a keen eye on test intelligence, especially the nuts and bolts of their commands and the resultant errors. However, it is usually a painstaking manual process that eats away a lot of what could potentially have been development time,” said Mayank Bhola, Co-founder and Head of Product, LambdaTest. “With our AI-powered command logs analytics, development teams now have an intelligent assistant that can do the heavy lifting and they can, in turn, focus on the decision-making. This will result in faster bug fixes, higher-quality software development, reduced time-to-market, and enhanced end-customer satisfaction.”