Tricentis this week made generally available a copilot add-on for its Tricentis Tosca test automation platform. The add-on simplifies finding, understanding, and optimizing tests via a chat interface.

Mav Turner, chief product and strategy officer for Tricentis, said the company is adding copilots to each of its product offerings using large language models (LLMs) developed by Open AI. Previously, Tricentis launched Tricentis Testim Copilot; the company plans to add a Tricentis Copilot for Tricentis qTest shortly .

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Over time, those AI assistants will be able to collaborate on tasks with each other and eventually with AI assistants provided by other providers of other platforms, including SAP, Turner added.

In the meantime, DevOps teams can use Tosca Copilot to find unused test cases, duplicate tests, unlinked assets, specific executions, and tests linked to application elements. Turner noted that the summarizations enabled by Tosca Copilot should make it simpler for DevOps teams to onboard additional testers.

DevOps teams can also make changes or modifications to any combination of those tests via a generative AI prompt rather than having to directly master the existing Tosca Query Language. That capability should also significantly reduce the time and effort currently required to troubleshoot test defects, said Turner.

Overall, Tricentis reports that time spent on complex testing activities can be cut in half by, for example, reducing the number of repetitive testing tasks previously required. In fact, Tricentis is already seeing a 16% to 43% reduction in test failure rates with Tricentis AI tools and up to a 50% increase in test case generation.

The goal is to not necessarily to increase the number of tests being run as much as it is to make sure the right tests are being conducted at the right time to improve application quality, said Turner.

In will, however, also become more feasible for any member of a DevOps team — including application developers — to iteratively generate a test. That’s better than having to wait on a dedicated testing team to create a test. Generative AI should also make it easier for DevOps teams to reuse tests, run tests faster, generate fewer errors, reduce costs and increase overall productivity.

It’s not clear yet to what degree generative AI will democratize application testing, but as tests become easier to create there should be more time to run a wider range of tests that might, for example, address cybersecurity issues. Most of the tests that are run today typically surface common programming mistakes and there is still a tendency to skip testing any time an application development projects starts to fall behind schedule.

However, as testing becomes faster in the age of AI there should be more time to run a wider range of tests. The challenge is finding the best way to orchestrate those tests across a portfolio of applications that is only going to become larger as AI tools make it simpler for developers to write code faster.