TestRail launches AI test script generation in beta

TestRail has launched AI Test Script Generation in version 10.2 of its test management platform. The feature is available in open beta for TestRail Cloud customers.

The tool converts manual test cases into automation code and related project files within TestRail. According to the company, it can replace 30 to 60 minutes of manual setup work per test by generating a first draft in seconds.

The release addresses a common bottleneck for software testing teams. Many organisations have increased investment in automation, but engineers still spend substantial time creating repeatable boilerplate code before they can focus on the specific logic of a test.

The feature is intended to preserve the link between documented test cases and automated scripts. That traceability is a central issue for quality assurance teams that need to show how tests map to requirements and execution records.

Users start with existing manual test cases already stored in TestRail. The system then produces structured automation code, adds project files and dependencies, and provides placeholders and guidance for engineers to adapt the output to their own environments.

Users can also refine the generated code through an interactive chat interface rather than restarting the process. The output can then be downloaded as a zipped implementation for use in existing automation projects and frameworks.

The launch comes as testing teams face pressure to broaden automation coverage without increasing engineering effort at the same pace. Repetitive setup tasks can limit how many tests a team automates and divert specialists from more complex work.

"We've seen a significant increase in automation adoption, but effective execution remains a challenge. Engineers are spending too much time rebuilding the same foundations for each new test, instead of focusing on what actually improves quality," said Sara Moura, Product Manager at TestRail.

"AI Test Script Generation turns existing test cases into structured automation code and ready-to-use project files. Engineers can generate a first draft in seconds, refine it through AI guidance, and focus on improving logic, expanding coverage, and delivering real business value."

Shared AI layer

The feature is based on Sembi IQ, a shared AI platform used across Sembi's software quality and security portfolio. Sembi describes it as a central intelligence layer designed to support the rollout of AI functions across multiple brands while using the context already present in each workflow.

That approach reflects a wider pattern in enterprise software, where vendors are embedding AI features into established products rather than asking customers to adopt separate tools. In quality assurance, this can be especially relevant because test data, case histories, and process records are already held in test management systems.

Katrina Collins, AI Product Manager at TestRail, said the company is focusing on practical uses for AI in testing workflows.

"With Sembi IQ, TestRail is focused on delivering AI capabilities that solve real problems in modern QA and automation workflows," Collins said.

"We take an iterative approach, starting with practical, high-value use cases and improving them based on feedback from QA and automation teams. It's built by QA for QA, with practical design choices that align with how teams actually work."

The latest release positions code generation as an entry point rather than a complete automation process. Engineers still need to review, adapt, and implement the generated output in line with their systems and coding standards, but the aim is to reduce the repetitive work needed to get started.

TestRail indicated that the release is part of a broader effort to add more AI functions to its platform. Those areas include test selection, prioritisation, business intelligence, and analytics for teams seeking to manage larger automation programmes.

"AI Test Script Generation is a first step toward deeper automation assistance, and we're continuing to invest in areas like AI test selection, prioritization, and real-time business intelligence and enhanced analytics to help enterprises scale automation more effectively," Collins said.