The main use case for Digital.ai Continuous Testing has been automating test execution as part of the CI/CD pipeline, especially for ensuring builds are stable before the release. For example, I used it to run the automated test suite whenever a new build was deployed. It helped me execute tests across different environments and quickly identify any failures earlier in the pipeline. It made it easier to catch issues before they even reach production and also improved the overall release quality. Digital.ai Continuous Testing is mainly a tool to ensure continuous testing for faster feedback and better reliability in the deployment. One of the key use cases for Digital.ai Continuous Testing is how it fits into the overall testing workflow, not just running tests. In my workflow, once a new build is triggered in the CI/CD pipeline, Digital.ai Continuous Testing automatically picks it up and runs the relevant test suite based on the stage, starting with a smoke test and then regression tests. After execution, it provides detailed reports and logs which help quickly identify where things failed. I have also used it to prioritize tests, so only critical ones run early for faster feedback, and the full suite runs later. This really helps speed up the release cycle. Another useful part is how it integrates with other tools, so the results can be shared within the team and issues can be tracked immediately. Overall, it acts as a continuous feedback loop to trigger tests, analyze, and improve, which makes the whole development and testing process more efficient.
Digital.ai Continuous Testing offers automated testing with real device cloud access and CI/CD integration to efficiently manage web and mobile app testing, ensuring reliability and consistency.Digital.ai Continuous Testing is designed to streamline testing by automating executions across multiple environments and devices, reducing manual efforts and speeding up feedback cycles. With features like cross-browser testing, and integration into CI/CD pipelines, it helps detect defects early and...
The main use case for Digital.ai Continuous Testing has been automating test execution as part of the CI/CD pipeline, especially for ensuring builds are stable before the release. For example, I used it to run the automated test suite whenever a new build was deployed. It helped me execute tests across different environments and quickly identify any failures earlier in the pipeline. It made it easier to catch issues before they even reach production and also improved the overall release quality. Digital.ai Continuous Testing is mainly a tool to ensure continuous testing for faster feedback and better reliability in the deployment. One of the key use cases for Digital.ai Continuous Testing is how it fits into the overall testing workflow, not just running tests. In my workflow, once a new build is triggered in the CI/CD pipeline, Digital.ai Continuous Testing automatically picks it up and runs the relevant test suite based on the stage, starting with a smoke test and then regression tests. After execution, it provides detailed reports and logs which help quickly identify where things failed. I have also used it to prioritize tests, so only critical ones run early for faster feedback, and the full suite runs later. This really helps speed up the release cycle. Another useful part is how it integrates with other tools, so the results can be shared within the team and issues can be tracked immediately. Overall, it acts as a continuous feedback loop to trigger tests, analyze, and improve, which makes the whole development and testing process more efficient.
I'm using Digital.ai Continuous Testing to create and test a mobile application. We're developing and testing a mobile app.
I work for a government-based company and we are able to use Experitest to perform testing on our native app.
We are mainly going to use it for accessibility testing on mobile devices. It is a cloud-based service with a desktop tool connected to it.