Mining user feedback for quality signals
Have the model read app-store reviews, support tickets and forum posts at volume and pull out the quality problems users are actually reporting, grouped and counted.
How it works
Feedback channels hold a large amount of quality information in a form nobody has time to read. The model classifies each item — bug report, usability complaint, performance, feature request, praise, noise — and extracts the specifics: which feature, which platform or version, what the user was trying to do. Grouping similar complaints and counting them turns hundreds of messages into a short list of recurring problems, with real quotes attached. Comparing periods, such as before and after a release, shows which problems are new. The output feeds test priorities: if users keep hitting a flow the test suite barely covers, that is a gap.
When to use it
After releases, to see what users noticed that testing didn't; when planning test priorities for a product with an active user base; when support and engineering disagree about what the big problems are; and on products where testers rarely see real usage.
Limitations
Feedback is skewed towards people who complain, and towards problems that are visible and annoying rather than ones that are silent and harmful — wrong data that looks right generates no reviews. Classification of short, angry, sarcastic or non-English text is unreliable, and counts inherit those errors. Personal data in support tickets needs handling before it goes anywhere near a model, under whatever terms your organisation has for it.
Watch for
One problem described in many different words, or in other languages, is split across small groups or filed as noise, so it ranks as minor while one loud complaint tops the list. Read a sample of what it labelled noise or praise, and look at the small groups, not only the top.
What you still own
Deciding what the patterns mean for testing, and reading enough of the raw feedback yourself to trust the groupings.
Cite this
Qlarify Labs. (2026). Mining user feedback for quality signals. Retrieved from https://labs.qlarify.fi/ai-in-testing/mining-user-feedback-for-quality-signals