How Do I Know If My Tests Are Good Enough? Coverage vs Value

Your tests are good enough when they fail if a real user would be hurt, not when a coverage number is high. A suite can cover 90% of the code and catch no real bugs, because coverage measures what the tests touch, not what they prove. The honest check: break a feature on purpose and see whether a test goes red. If none does, the suite is decoration.

If you would rather have the gaps found and filled for you, see how RAITHub would test this below, or the QA and test automation overview.

Isn't high coverage the same as good tests?

No, and this is the trap. Code coverage measures how much of your code runs while the tests execute. It says nothing about whether the tests check the right outcome. A test can run a function, assert nothing meaningful, and still count toward coverage. AI-written tests make this worse, because they tend to assert whatever the code already does, so they pass by construction; the full version is in why AI-written tests pass but miss bugs.

Coverage is useful as a floor, not a target. Low coverage tells you something is untested; high coverage does not tell you it is tested well.

What does a good test actually prove?

A good test fails when the behaviour a user depends on breaks. Three properties separate a useful test from a decorative one.

A weak test	A good test
Asserts what the code already returns	Asserts what the user needs the code to return
Passes no matter what the code does	Fails when the behaviour breaks
Covers the happy path only	Covers the ways a real user breaks it
Tests one function in isolation, nothing joined up	Tests the journey a user actually takes

The journeys worth testing are the ones that lose money, data or trust: sign-up and login, the core workflow, payments, and whether one user can read another's data. A suite that is thorough on trivial functions and silent on those is not good enough, however green it looks.

What is the honest way to measure test quality?

Break the code and see if the tests notice. This is the idea behind mutation testing: deliberately introduce a bug and check whether a test fails. You can do it by hand in minutes. Flip a condition that should block access:

// change this real access check...
if (record.ownerId !== session.userId) return forbidden()

// ...to this deliberately broken version, then run the suite:
if (record.ownerId === session.userId) return forbidden()

If no test goes red after that change, you have just proved the suite does not protect data access. Repeat for a payment amount, a role check and the core workflow. Each silent mutation is a real gap. Which metrics actually tell you something, beyond coverage, is in QA metrics that matter.

What are the signs my suite is weak?

• It never fails. A suite that is always green either has no real bugs ever, which is unlikely, or does not check for them.
• It did not catch the last production bug. After any incident, ask whether a test could have caught it, then write that test.
• It is all unit tests, no journeys. Isolated functions can all pass while the joined-up flow is broken.
• Breaking a feature leaves it green. The mutation check above is the fastest way to find this.
• Tests are flaky and ignored. A test the team has learned to re-run until it passes protects nothing.

How to apply this to AI-generated code specifically is in how to test AI-generated code, and the related question of who should write the tests is in who can write tests for my app.

A practical way to use this list: after your next production incident, work backwards through it. Ask whether a test could have caught the bug, why the existing suite did not, and which of the signs above applied. Almost every incident maps to one of them, usually "it is all unit tests, no journeys" or "breaking the feature left it green." Writing the one test that would have caught that incident is the highest-value test you can add, because it is proof, not a guess, that the gap was real.

Buy, build or hire?

Route	Choose this when
A mutation-testing tool	You want an automated measure of which tests actually catch bugs, and will act on the gaps
Audit the suite yourself	You can spend a day breaking features on purpose and writing tests for what slips through
A freelancer to review the suite	You want an outside read on where the suite is thin, once
A managed QA team	You want the gaps found, filled and kept filled on every release, with tests you keep

How RAITHub would test this

RAITHub judges a suite by what it catches, not its coverage number, and fills the gaps that matter.

• A gap audit: map the journeys that cost most when they break, then check whether the current suite fails when they do, by breaking them on purpose.
• The right tests, not more tests: strengthen the suite where it is silent, especially cross-user access, payments and the core workflow.
• Tests that fail honestly: no assertions that pass by construction, and flaky tests fixed rather than ignored.
• Tests you own: everything lands in your repository with the IP assigned to you.

Timeline: a suite audit is scoped and dated before it starts; ongoing work runs month-to-month. For proof, PropDesk runs 1,024 tests, Sundor Skin 530+ including a suite that tries to read other buyers' data, and this website 400+ in CI. See QA as a Service. The next step is a free 15-minute audit, then a written fixed quote; RAITHub publishes no rates.

Not sure your tests are catching real bugs? Ask for a test-suite review.

Frequently asked questions

How do I know if my tests are good enough?

They are good enough when they fail if a real user would be hurt. Break a feature on purpose and see whether a test goes red. If none does, the suite does not protect that feature, regardless of how high the coverage number is.

Is 100% test coverage the goal?

No. Coverage measures what the tests touch, not what they prove. A suite can hit high coverage with tests that assert nothing meaningful. Use coverage as a floor to find untested code, not as a target that implies the code is tested well.

What is mutation testing?

Deliberately introducing a bug into the code and checking whether a test fails. If the suite stays green after you break a feature, it is not testing that feature. You can do it by hand by flipping a condition, or with a mutation-testing tool for a systematic measure.

Why do my tests pass but bugs still reach users?

Usually because the tests assert what the code already does rather than what users need, cover only the happy path, or test isolated functions instead of real journeys. This is especially common with AI-written tests, which tend to pass by construction.

What should I test if I can only write a few tests?

The journeys that lose money, data or trust: sign-up and login, the core workflow, payments, and whether one user can read another's data. A handful of tests over those paths is worth far more than many tests over trivial functions.

How often should I re-check whether my tests are good enough?

After every production incident, ask whether a test could have caught it and add that test. Beyond that, a periodic mutation check on the critical paths keeps the suite honest, because tests decay as the code changes around them.