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 flaky 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?
Yes, I’ve seen AI make a huge difference in keeping tests resilient. It can detect changes in locators or deviations in workflows and adjust your tests automatically. Tools like Kane AI or LambdaTest’s Smart Locator repair broken tests on the fly while logging every change so you can review them. This means your automation suite stays reliable even as the application evolves.
In one session I encountered a race condition triggered by overlapping API calls. It didn’t appear in local runs but failed intermittently in CI because of timing differences. In situations like this, retries can be useful for transient issues, but it’s important to flag tests as flaky rather than treating them as genuine bugs.
Flakiness is defined statistically intermittent failures at a noticeable rate indicate instability that warrants investigation. AI can help by analyzing historical test runs, code changes, and environmental factors to predict potential flaky tests, though it’s not perfect.
Yes, I’ve found that retries can be useful for handling transient issues, but it’s important to flag those tests as flaky rather than treating them as real bugs.
This distinction helps teams track patterns over time and identify underlying instability instead of chasing false alarms.
By making flakiness visible, you can prioritize fixes where they matter most and prevent wasted effort on issues that are not reproducible, keeping both your test suite and your team’s focus in good shape.
Flakiness is something I’ve seen trip up even experienced teams. It’s defined statistically, meaning that if a test fails intermittently at a noticeable rate, it signals instability that needs attention.
I’ve found that treating these failures as one-off events can hide real problems, so it’s better to track them, understand patterns, and address the underlying causes.
Doing this not only keeps your test suite reliable but also prevents those frustrating surprises from popping up in CI or production. It’s a small habit that pays off in long-term confidence.
I’ve found that AI can be a big help in spotting potential flaky tests. By analyzing historical test runs, code changes, and environmental factors, it can highlight areas that are likely to fail intermittently.
In practice, it’s not perfect and shouldn’t replace hands-on investigation, but it can save a lot of time by pointing you to patterns that might otherwise go unnoticed. Combining AI insights with team review usually gives the best results, helping you catch instability before it becomes a bigger problem.
In my experience, the key to managing flaky tests is visibility and ownership. Teams should track them in dashboards, bring them up during stand-ups, and assign someone to investigate or monitor them.
When flakiness is treated as a visible, shared responsibility rather than an annoying footnote, it becomes much easier to spot patterns, prioritize fixes, and prevent these intermittent failures from undermining confidence in your test suite.