Software delivery becomes difficult to manage when development, testing, security and deployment rely on disconnected tools and manual handoffs. Enterprise teams need visibility into application work from requirements through release, alongside reliable ways to identify defects, manage vulnerabilities and validate performance.

OpenText [NASDAQ: OTEX] brings these activities together through its Core Software Delivery platform, a common SaaS environment for software delivery management, quality management and testing. More than 250 integrations connect the platform with existing development and delivery technologies, allowing enterprises to extend automation without replacing established tool chains.

DevOps Aviator introduces AI assistance, drawing on information already managed within the platform, including requirements, features, defects, tasks and tests. Teams can generate user stories, create test suggestions, analyze defects and obtain delivery insights based on the context surrounding their work. OpenText’s approach places AI within existing software processes rather than treating it as a separate destination.

Keeping Security within the Delivery Process

AI-assisted testing forms one part of OpenText’s broader software delivery model. DevOps Aviator can generate test scenarios from application stories and existing project information, giving quality teams a starting point grounded in the software they are preparing to release. Human testers remain responsible for reviewing and validating the resulting cases before they enter subsequent stages of the workflow.

OpenText also applies AI to application security through threat modeling. DevOps Aviator uses the STRIDE framework to identify potential threats based on application architecture and design. Developers can examine security concerns during development and use the findings to inform testing and remediation, connecting security considerations to application design rather than postponing them until a later review.

Fortify’s portfolio extends this focus through application security testing and vulnerability management. Within the portfolio, Fortify Remediation Aviator adds AI-supported assistance for addressing vulnerabilities in code, helping developers understand possible remediation approaches. Security findings can consequently feed into development activities, giving teams a more direct connection between identifying a flaw and working on its resolution.

Changing How Teams Approach Test Creation

Pick n Pay provides a practical example of how AI can alter an established testing workflow. The retailer selected application stories from a release, moved them into a sandbox within the Core Software Delivery platform and used DevOps Aviator to generate test scenarios.

Pick n Pay’s team compared the AI-generated scenarios with manually created test cases and found test ideas that had not appeared in the manual work. A comparison with ChatGPT also found the OpenText-generated suggestions more detailed and less generic, as they drew on historical information available within the platform.

Historical information associated with Pick n Pay’s work gave DevOps Aviator context for generating suggestions. Requirements and delivery context informed the output, while human testers still reviewed and validated the generated cases before they moved forward.

Pick n Pay reduced the time required to write test cases and hand them to its automation team from seven days to four. Release frequency subsequently reached four to five releases per week across 17 omnichannel applications, compared with roughly one release every six months previously.

OpenText also supports the conversion of manual tests into automated assets through codeless workflows and Gherkin-based behavior-driven development.

Preparing Applications for Demanding Conditions

Functional testing and performance engineering address different questions. Functional testing examines whether an application behaves as expected, whereas performance testing examines how it responds under particular workloads. OpenText brings both activities into its software delivery portfolio, allowing teams to assess application behavior from different perspectives.

Discount Tire’s use of LoadRunner Enterprise illustrates the role of performance testing before a demanding business event. The retailer established a formal preparation process after encountering performance issues during an earlier period of sudden traffic growth. Its testing activities now examine customer-facing applications before Black Friday, when increased demand makes application responsiveness especially important.

Applications are tested with three times the number of users recorded during Discount Tire’s previous Black Friday period. Results from those tests give developers information about application behavior under heavier workloads before the peak event arrives. Discount Tire has also explored closer integration between performance tests and CI/CD processes to bring performance feedback earlier into development.

Functional testing is also moving toward unattended execution. OpenText Core Software Delivery platform functional testing 26.2 introduced the Autonomous tester, which can execute existing manual tests without requiring teams to create automation scripts for every case. The release also introduced AI response evaluation and new model-based testing capabilities.

Autonomous tester changes the role of existing manual test libraries. Instead of requiring every test to be rewritten as a script, teams can assign eligible manual tests for execution and retain human oversight of the results.

Giving Delivery Teams More Predictive Insight

OpenText’s AI capabilities extend beyond test generation and execution into delivery analysis. DevOps Aviator can use historical delivery information and velocity data to help predict feature timelines and provide insight into expected time to market. Such information gives teams another way to examine delivery progress alongside the work itself.

AI-powered session replays provide a separate source of application insight. Teams can analyze recorded user sessions to identify issues and turn relevant findings into defects or test steps. The capability connects observed user behavior with activities that may require investigation, testing or correction.

OpenText also supports organizations that want to bring their own AI models into DevOps workflows. Its bring-your-own-model approach allows customers to use preferred models within DevOps Aviator while maintaining control over enterprise information and the environment in which AI-assisted work takes place.

Extending AI through the Software Lifecycle

OpenText’s value-stream management capabilities give enterprise leaders a broader view of how software initiatives move from planning to production. The platform connects strategic priorities with delivery activity and provides information about progress, velocity, flow and returns across application initiatives.

That perspective adds a management layer to the technical work performed by development and quality teams. Leaders can examine delivery activity in relation to broader initiatives, identify areas requiring attention and understand how applications work to contribute to organizational priorities.

A connected approach also allows different teams to engage with AI and automation at different points in the lifecycle. Developers can use AI-generated suggestions and delivery insights. Quality teams can create test scenarios and automate existing manual cases. Security teams can introduce threat modeling and remediation assistance, while performance teams can evaluate applications under demanding workloads.

OpenText’s recognition as the Top AI-driven DevOps Automation Solutions in Canada 2026 reflects its approach to connecting AI-assisted development, testing, security and performance engineering within software delivery.