Can we do debugging for failed test cases in test execution
When debugging intermittent issues in real devices (battery drain, OS-level interruptions, push notifications), how effective is KaneAI in logging and reproducing them?
How does KaneAI handle sensitive client data while still leveraging AI for insights?
What happens for dynamic elements where locators keep changing?
What all inputs or accesses does KaneAI require to generate Test Cases?
Can the automation code generated be tailored for a particular automation framework?
How to debug intent-based test failures versus selector-based failures?
Can we perform Green Screen testing?
Can KaneAI support infrastructure testing
Do we also have an option to add few more tests of our own and have kane AI to generate an automated test out of it
Do we also generate test reports after the test runs? Also can it be integrated with Slack or emails?
Does KaneAI provide test coverage as well? And can we integrate with Jira?
Can KaneAI adapt to domain-specific testing needs, for example, in banking or healthcare projects?
Does KaneAI support testing of apps with languages other than English?
How we can Leverage KaneAI for Performance Tests?
Yes, KaneAI can connect with Figma, and that’s actually a really useful capability if you want to catch issues early.
What it does is pull out UI elements, design flows, and even component metadata directly from your prototypes. From there, it can auto-generate tests before a single line of code is written.
- This speeds up QA in the design stage, not just after development.
- You get a head start on automation since tests evolve as the design matures.
- A common pitfall is ignoring design updates, so always keep the Figma sync active.
It feels almost like shifting testing left without adding overhead.
KaneAI is a commercial product, built as a paid AI testing assistant.
The value comes from the integrations it supports with the tools teams already use - GitHub, GitLab, Jenkins, and other CI/CD pipelines.
So you get the freedom of customizing every line of code like you might with open source, you also can gain enterprise-ready support and faster setup.
I’m glad you brought that up, because one concern I hear a lot is, “How do I know what the AI actually changed in my tests?”
With KaneAI, every locator update, every decision path, and the reasoning behind it gets logged. You’re not left guessing.
- You can pull up a diff report to see the exact before-and-after.
- Those logs tie back to the original test definitions so you always have traceability.
- The key is to review these regularly - skipping audits is the fastest way for drift to creep in.
Yes, it can, and that’s actually one of the biggest advantages. KaneAI can take a feature requirement or even a design document and translate that into suggested test cases.
Think of it as having a junior tester who reads through the PRD or user story and immediately drafts a set of functional, regression, and sometimes edge-case scenarios.
- For feature requirements, it pulls out user flows and acceptance criteria.
- From design docs or Figma, it maps UI elements to possible interactions.
- Just remember, these are starting points - reviewing them with your QA leads ensures relevance and avoids over-testing trivial paths.
Good question, and I hear this concern often around security. KaneAI doesn’t need repo access. What it usually requires is access to the parts of your Git workflow that it needs to generate, update, and sync tests.