Friday, May 24, 2024

Tricentis announces Tricentis Copilot

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New AI-powered assistants help quality engineering teams and developers integrate AI responsibly to test even the most complex applications

Tricentis, a global leader in continuous testing and quality engineering, announced the latest update to the company’s AI offerings with the launch of Tricentis Copilot, a suite of solutions leveraging generative AI to enhance productivity throughout the entire testing lifecycle. The first Tricentis Copilot solution introduced and available today is Tricentis Testim Copilot, which makes it quicker and easier for users to create tests using generative AI.

With Testim Copilot, testers no longer need to write most of their own code. Users can simply enter a text description of their test, and Testim Copilot generates the needed JavaScript code using generative AI, provides an explanation of selected code, or identifies and recommends fixes for potential issues.

Unlike many general-purpose, generative AI tools, Testim Copilot is fully integrated with Tricentis Testim and built with the goal of making testing complex applications fast and easy.
Enterprise ready and scalable, it:

  • Enables teams to create tests for complex applications faster, using generative AI to generate custom code automatically from a text description
  • Makes it easier for less-technical testers to create custom tests without in-depth coding expertise
  • Explains test steps, making it easier to understand, document, and reuse existing code
  • Reduces the time and effort to debug test code by identifying issues and suggesting fixes

Additional Tricentis Copilot solutions for Tricentis Tosca and Tricentis qTest will be coming soon to provide an even easier, faster, and enhanced experience across the Tricentis quality engineering platform.

Also Read: SmartBear Integrates API Tools to Enhance Design Experience for Teams

With enterprises increasingly concerned about compliance, data use, and IP protection when it comes to utilizing generative AI in large language models (LLMs), Testim Copilot has been designed to provide productivity gains and increased application quality without compromising on responsible usage, safety, or accessibility.

“Based on initial customer experience with existing Tricentis AI-enabled products, we have seen an uplift of 20% to 50% in test case generation by leveraging AI,” said Mav Turner, Chief Product and Strategy Officer, Tricentis. “In addition, customers utilizing Tricentis AI tools have been able to lower their test failure rate by 16% to 43% so far. The democratization of testing with AI will allow even less technical resources to participate in the creation and execution of AI-generated test cases, leading to faster completion, fewer errors, higher productivity, and reduced costs.

“AI has been at the forefront of Tricentis’ product portfolio for several years, and the launch of Tricentis Copilot marks the next step in that journey,” Turner continued. “Testim Copilot puts AI directly into the hands of the user, automatically suggesting test cases and fixes, meaning more time spent on workflows to boost productivity and improve time to market for new applications. This is only the beginning – we expect future Tricentis Copilot releases to have even greater benefits.”

IDC estimates that enterprises will leverage generative AI and automation technologies to drive $1 trillion in productivity gains by 2026.

“Testing and automated software quality are a top area of expected benefit for GenAI over the next 12 months, according to IDC survey data, and we have seen a majority of respondents expanding use, using or piloting use of AI in conjunction with testing,” said Melinda Ballou, research director for IDC’s Agile ALM, Quality & Portfolio strategies. “Examples of areas of focus include test prioritization, identifying root cause of failure, automated test case creation and self-healing of test cases. Early investment in AI by test automation providers such as Tricentis and leverage of pragmatically actionable data as compared with the inefficiencies of manual testing position help drive adoption, better code quality and cost savings.”

Source: Tricentis

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