End-2-End Testing using Generative AI
Make your apps productive ready in real time!
A Visual Walk Through of the Roost.ai Platform
Ephemeral Environment Creation and Management
Effortlessly create an integration test environment at every pull request, feature branch, or insertion point in the DevOps / GitOps pipeline using the same micro services, containers, sidecars, etc. as production.
Each environment is a full stack testing environment, custom-built for a specific code change and exists for its functional use and automatically disappears after merge.
Leverage Your Existing Infrastructure as Code Scripts
Use your components defined in Terraform or CloudFormation scripts ensuring that the environment is created the way it was originally architected.
AI-enabled Auto-Discovery of Your Environment & Automated Testing
Roost's AI-enabled platform auto-discovers environment configuration by inspecting source-code repositories (e.g. GitHub, GitLab, BitBucket) and then automatically validates the state of containers.
These states can be merged to production or used for rollback if necessary.
Simplify your build processes using Drag and Drop
Quickly and easily build error-free containers with the Roost drag and drop feature.
User’s don’t need to remember the Dockerfile syntax and can avoid making errors by utilizing built-in Dockerfile templates.
Share Preview Environments
Once all the tests are done, a preview environment can be shared via a custom URL with all stakeholders, including the DevOps team, SREs, QA, and product management, to provide real-time feedback and validate their deliverable.
Traditional testing environments aren't enough
A traditional test environment allows developers to do basic testing and validation of code; however, the static nature of a most testing sites makes it impossible for efficient testing to occur when dealing with modern complex architectures such as containers, micro services and cloud-native applications.
Because of the static nature of testing environments they will never meet the demand of evolved artifacts and versions of services required by a pull request.
Roost's AI-enabled automated testing to the rescue
Roost's AI-enabled platform auto-discovers environment configuration by inspecting source-code repositories (e.g. GitHub, GitLab, BitBucket) and then optimizes it using the power of machine learning. This is Roost's "secret sauce" to avoid integration issues later in production and reducing change failure rates.