The test runs properly in the local environment but is causing flaky behaviour in the CI? what are the potential factors for the same?
Can AI predict which tests will fail intermittently?
How can network latency or environment dependency be reduced in cloud testing?
Can AI-powered testing tools themselves be developed and deployed inclusively, including bias and training and cultural sensitivity factors?
What role does test data management play in preventing flaky test runs?
Which strategies, approaches or tools do you use to prioritize tests to run minimal tests?
What metrics reveal the real impact of flaky tests on developers?
Is it okay to .skip a flaky test as long you make a JIRA ticket or something similar?
What should be done if the flaky test is also a smoke test?
How does CI/CD integration with LambdaTest handle retries for flaky tests?
What metrics or dashboards best help track flaky test trends over time?
Can we automatically fix failed test like self healing through AI?
Can we have a workaround in the script coding to overcome the Flexy tests? Even if we know that coding is not smart?
In your experience, how do infrastructure aspects (Kubernetes pods, network latency, DB state, caching, parallelization) contribute to flakiness, and how do you mitigate them?
Can we have a workaround in the script coding to overcome the Flexy tests? Even if we know that coding is not smart?