QlarifyLabs
← AI in Testing
Keeping automation healthyEmerging

Pruning and de-duplicating suites

Use coverage data and run history to find where the suite carries dead weight, and have the model explain, test by test, what would be lost if it went.

How it works

Test suites grow faster than anyone reviews them. The model reads tests together with coverage data and run history and looks for redundancy: tests with near-identical steps and assertions, tests whose coverage is entirely contained in another's, tests that have never failed in years and assert only trivial things, tests that reference removed features or are permanently skipped. For each candidate it explains the reasoning and what would be lost by removing it. Mutation testing can give evidence that removing a group of tests doesn't reduce what the suite catches. Proposals go through normal code review; nothing is removed automatically.

When to use it

Suites that have become too slow or costly to run; inherited test code with no clear owner; after a product area is retired or rebuilt; and before migrating a suite to a new framework, so the dead weight isn't migrated with it.

Limitations

A test that never fails may be guarding against a rare but serious regression, and history can't distinguish that from a useless test. Two tests that look alike may cover different configurations or data. The model's sense of what a test is for comes from its name and code, which are often misleading in old suites.

Watch for

A test that hasn’t failed in years is removed as dead weight; it was added after a leap-day billing incident, and the next leap year finds the bug in production. Read each candidate’s commit history and linked tickets before removing it.

What you still own

Every removal decision, and knowing which old tests exist because of a past incident.

Cite this

Qlarify Labs. (2026). Pruning and de-duplicating suites. Retrieved from https://labs.qlarify.fi/ai-in-testing/pruning-and-deduplicating-suites