DevOps Aviator brings generative AI into software delivery to help test teams move sooner, reduce manual effort, and get answers faster. It is part of the broader Aviator suite: a set of AI capabilities embedded across OpenText products.
Unlike generic chatbots, it works inside governed business systems. This is important for organisations that need stronger control over security, compliance, and how business data is handled.
Why DevOps Aviator Matters
OpenText’s Pick n Pay case study shows 95% software test automation, up to three days saved per cycle in testing time, and 20% additional coverage on platform-specific scenarios after using DevOps Aviator.
The workflow shift mattered as much as the raw numbers.
Pick n Pay used Aviator to generate test cases from user stories and features, compared the results with manual testers, and found that the AI suggestions often matched the human output while still surfacing valid test cases the team had missed.
The approach helped reduce the time spent writing test cases and freed testers to focus on exploratory testing and scenario analysis.
Eight Ways DevOps Aviator Helps Test Teams
DevOps Aviator turns test tooling from a static repository into an AI-assisted workbench that can help teams move from idea to validation more quickly.
Here are eight ways it helps:
1. AI-powered smart assistant
Ask questions about features, defects, tasks, or tests and get plain-English answers without jumping between tools. For example, a test lead can ask what changed in a release, which tests are linked to a feature, or which defects are still open.
2. AI-powered test conversion
Turn manual tests into automated assets more quickly, using either codeless workflows or Gherkin BDD. A tester can take a high-value regression scenario and ask Aviator to convert it into a structured automated starting point instead of rebuilding it from scratch.
3. AI-powered time-to-market predictions
Use historical velocity and delivery data to estimate feature completion and release timing more confidently. This gives product and QA teams a better sense of risk when planning sprint commitments.
4. AI-powered session replays
Review user recordings and convert the useful parts into defects or test steps more quickly. For example, if a session replay shows a checkout failure, the tester can turn that evidence into a defect rather than manually reconstructing the problem.
5. AI-generated test suggestions
Generate test ideas and steps to broaden coverage and reduce gaps. This is useful when a feature is new and the team wants a quick first pass before refining the suite.
6. AI-generated full-stack testing
Use AI-assisted generation to build more repeatable tests across layers, including JUnit, Java, Selenium, and UI automation. That helps teams create usable test assets faster, especially when the same feature needs regression coverage at multiple levels.
7. AI-generated threat modelling
Apply AI to security design reviews and STRIDE-style thinking earlier in the lifecycle. A team can use it to surface likely risks before the feature reaches production.
8. AI-generated defect analysis
Summarise conversations, identify likely root causes, and suggest next steps faster. That shortens triage time when multiple defects are coming in at once.
How DevOps Aviator Can Change Your Test Processes
The biggest shift is that testing becomes assisted by a trusted AI solution, and less dependent on repetitive manual authoring or personal chatbots.
Instead of treating test tools as places to record and run tests, teams can use them to generate tests, propose scenarios, summarise defects, and reduce analysis time.
That changes the workflow in a few concrete ways.
Test design starts earlier, automation can begin before the manual testing cycle is complete, and testers spend less time writing routine cases from scratch. It also helps developers, testers, and managers work from the same source of truth, which reduces the usual back-and-forth between tools and people.
A Simple Example
Instead of waiting for a manual tester to finish a first pass on a new checkout feature, the team can ask Aviator to generate scenarios during sprint planning. The tester then reviews, edits, and prioritises those scenarios rather than building them from a blank page.
In practical terms, it lets you test earlier, faster, cheaper, and more comprehensively.
