When someone says, “We need better test coverage,” how do you understand it?
The truth is, it can mean very different things to different people. Sometimes they talk about how much of the codebase gets executed during tests. Other times, they mean how much of the system’s functionality is verified. And occasionally, what they really want is for tests to catch more defects before users do.
These three ideas, code coverage, test coverage, and test effectiveness, sound similar but measure very different aspects of software quality:
- Code coverage looks at the percentage of source code executed when your test suite runs. It answers the question: Did we test this line or branch of code at all?
- Test coverage measures how many of the system’s requirements, features, or scenarios are being tested. Instead of focusing on code, it asks: Are we testing the right things the user cares about?
- Test effectiveness evaluates whether the tests are actually useful in practice. It’s about outcomes: Are our tests catching bugs, preventing regressions, and improving confidence in releases?
Because these terms often get used interchangeably, teams can end up chasing the wrong numbers. In this post, we will break down what each of these metrics truly means, where they overlap, how they differ, and, most importantly, how to move from chasing percentages to driving real quality improvements.
Code Coverage: Measuring Execution
Code coverage is the most technical and tooling-friendly measurement. It shows how much of your actual code (lines, branches, conditions) has been executed during tests. It’s a numeric, objective indicator: if your tool reports 78%-line coverage, that means 78% of the lines of code were hit during testing.
Key characteristics:
- Quantitative, easy to automate
- Focuses on what code ran
- Does not confirm whether it ran correctly
Types include:
- MC/DC (Modified Condition/Decision Coverage)
Test Coverage: Measuring Intent
Test coverage goes a level higher. It is about what your tests are designed to validate, not just what code they incidentally touch. Think of it as the intent-to-code connection:
- Are you covering all the relevant user roles, error states, edge cases, and inputs?
- Are your test cases aligned with what users, stakeholders, or regulations actually expect the system to do?
Examples of test coverage questions:
- Do we have test cases for every access level (e.g., admin, guest, external user)?
- Are all API endpoints tested with valid and invalid inputs?
- Are exception paths, timeouts, and edge logic tested?
Unlike code coverage, this is not always automated. It often involves requirements mapping, manual test design, and domain knowledge.
Test Effectiveness: Measuring Value
Now we arrive at the most important, and often least measured, concept: test effectiveness.
It’s about whether the test suite is doing its job finding bugs, preventing regressions, and supporting releases.
Indicators of high-test effectiveness:
- Defects are caught early, not in production
- Test failures are clear and meaningful
- Test cases cover risky, complex logic (not just “happy paths”)
- Test effort stays proportional to system complexity
You could have 95% code coverage and still miss the one condition that causes a security failure, a memory leak, or a crash on edge hardware.
Combining Code Coverage, Test Coverage, and Test Effectiveness Metrics
To build quality software, you need all three metrics: code coverage, test coverage, test effectiveness, and a workflow that connects them. Without that connection, coverage numbers can be misleading, test cases may miss critical logic, and quality risks can go undetected.
Most teams track coverage with a single number. Maybe it says 85%. Maybe even 95%. But the reality is, those numbers often hide more than they reveal. A line can be “covered” without the important logic inside it ever being tested. An impressive percentage can still leave error paths, edge cases, or decision branches untouched.
This is where Coco makes a difference. It does not just measure whether code was executed, it looks at the decisions inside the code. With support for detailed metrics like MC/DC and condition coverage, Coco shows whether both sides of a logical condition (true and false) were actually exercised. For industries where safety and reliability are not negotiable, that level of visibility is essential.
And there is another layer most tools miss: Coco shows which tests executed which parts of the code. That means you are not left guessing why something was (or was not) covered. You can see exactly what each test contributed. For anyone writing or maintaining tests, that context is invaluable: it tells you whether your test suite is truly effective or just broad but shallow.
While Coco does not replace a test case management system, it plays a vital role in verifying execution. You may believe you’ve tested every role or scenario, but Coco can confirm whether the underlying code paths actually ran. In practice, Coco acts as a bridge between test design and code execution by:
- Validating that your test plans hit the intended logic
- Revealing coverage depth, not just line hits, to assess test effectiveness
- Linking coverage data back to specific tests, so improvements are actionable
The outcome is simple but powerful: instead of chasing percentages, you gain a clear picture of what is genuinely tested, what is not, and how to close the gaps. Whether you’re working in C++, QML, or embedded C, Coco gives you the confidence that your coverage metrics reflect real quality, not just a number in a report.
Conclusion
So next time someone says, “We have 85% coverage,” ask the critical follow-up: “Coverage of what?”
- Code lines?
- Business logic?
- User flows?
- Risk areas?
A single percentage cannot tell you the full story. The real value comes from knowing what is being covered, why it matters, and what is still missing.
The best teams do not measure just one layer. The best modern teams connect code coverage, test coverage, and test effectiveness into a feedback loop. This ensures that tests are not only running code but also validating the right logic and reducing real-world risk.
If you’re using Coco, you are already equipped to close that loop. Coco helps you:
- Understand what has been tested
- See what has not been
- Improve where it matters most
Because in the end, the goal is not just more coverage, it is better software.
Want to know what your coverage percentage actually means?
Numbers like 70%, 80%, or even 100% do not tell the whole story, and sometimes they can create a false sense of security. In our deep dive, “Is 70%, 80%, 90%, or 100% Code Coverage Good Enough?”, we break down what those metrics really say about your tests, why 100% statement coverage can still leave dangerous gaps.
See how you can use GitHub Copilot + Coco Code Coverage to raise test coverage from 65% to 78%, and how this approach can be adapted for other frameworks and industries, including safety-critical domains.
Want to see what your current tests are missing?
Read more about Coco Code Coverage
