When your test suite passes and your coverage report shows high percentages, it’s easy to assume your code is well tested. In reality, code coverage metrics often provide a false sense of confidence.
Code coverage measures which parts of your source code are executed by tests but execution alone doesn’t guarantee correctness. To avoid costly blind spots, it’s essential to understand what coverage actually measures, what it doesn’t, and how to use it effectively.
Definition
Code coverage tools, such as Coco, provide detailed reports showing which statements, branches, and conditions were exercised. These insights enable teams to improve test completeness and maintain robust software quality.
Understanding what you’re measuring with code coverage
Code coverage shows which parts of your source code executed when you ran your tests. Think of coverage as playing a role for testers similar to what a debugger does for developers:
-
Developers use debuggers to isolate the specific lines of faulty code
-
Testers use coverage to isolate untested pathways and uncover hidden defects and vulnerabilities
When a test exercises specific code, that code is considered covered. Coverage helps identify untested sections, potentially dead code, and areas where redundant tests can be removed.
However, there’s a critical distinction many teams miss: coverage shows which code ran, not whether that code was verified correctly. A function can execute completely while your tests assert nothing meaningful about its behavior.
The coverage hierarchy and what each level reveals
Function coverage
Measures whether functions were called and how often. Achieving 100% function coverage confirms that all functions ran at least once, making it useful for spotting dead or unused code. However, it does not confirm that any function behaved correctly.
Line coverage
Measures which lines containing executable code were executed. This metric is heavily influenced by formatting: a single line may contain multiple statements, and identical logic formatted differently can produce very different results. As a result, line coverage is unreliable for comparisons or enforcement across teams.
Statement coverage
Statement coverage addresses formatting issues by grouping statements that are always executed together and ensuring all executable statements are exercised, regardless of how the code is formatted.
In the Coco Code Coverage tool's documentation, this is often referred to as statement block coverage, since it groups statements into blocks that are always executed together.
Statement coverage produces more consistent metrics across coding styles but it still doesn’t ensure that all logical paths have been tested.
Go deeper in understanding the meaning behind the code coverage percentages from this expert blog. We’ll unpack what those numbers really mean, what 100% statement coverage does (and does not) guarantee.
Decision (branch) coverage
Decision coverage requires tests for both outcomes of every decision (true and false). This is where coverage analysis starts uncovering meaningful gaps: many teams test only “happy paths” and miss error handling. Decision coverage forces validation of both paths and often reveals logic errors that simpler metrics miss. For most teams, this metric strikes the best balance between practicality and insight.
Condition coverage
Condition coverage breaks complex decisions into individual conditions. Instead of testing only “access granted” versus “access denied,” it verifies that each contributing condition (for example, logged in, permission granted, not expired) has been evaluated as both true and false, often exposing unexpected gaps.
Multiple Condition Coverage (MCC)
MCC requires testing every possible combination of conditions in a decision. With three conditions, that’s eight scenarios; add two more and you’re at 32. MCC can explode in complexity quickly, which is why it’s used selectively.
Modified Condition/Decision Coverage (MC/DC)
MC/DC offers a smarter balance: rather than testing every combination, it requires proving that each individual condition independently affects the decision outcome. You don’t need to try every ingredient combination to know a recipe works; you just need to show that removing each ingredient changes the result. MC/DC does the same for code, delivering strong assurance without thousands of scenarios, and it’s considered the gold standard in safety‑critical software.
For a deeper discussion of these coverage levels and how they’re applied in regulated environments, watch James Vance, Senior Software Engineer at Qt Group. James breaks down how to approach testing with code coverage in mind, explains the key levels of code coverage, and highlights what safety-critical industries require to ensure high-confidence, compliant software.
Access the full webinar via this link to learn more about code coverage in practice, including:
-
The benefits of integrating code coverage into your testing process
-
The limitations of code coverage and how to account for them
-
Criteria for selecting a code coverage tool
-
Key features of the Coco Code Coverage tool
-
A live demonstration of Coco
When coverage levels are mandated by industry
For teams building safety‑critical systems, thorough testing isn’t optional—it’s required.
- Automotive (ISO 26262): Decision/branch coverage is recommended (and highly recommended at higher ASILs), and MC/DC is highly recommended at ASIL D (with recommendations at lower ASILs), depending on risk.
- Railway: EN 50128 historically recommended branch coverage and MC/DC or MCC, increasingly strongly at SIL 3–4. The railway standards have evolved—EN 50716:2023 supersedes EN 50128 and tightens verification expectations, including tool qualification, so teams should align new projects with EN 50716 while maintaining legacy systems appropriately.
Even outside regulated industries, these standards offer valuable guidance born from decades of experience in catching mission‑critical defects before they cause harm.
What code coverage can (and can’t) do for your team
| Code Coverage Good at |
Where code coverage falls short |
|
Catch bugs early Coverage analysis reveals untested code before it fails in production. |
High coverage ≠ high‑quality tests You can reach 100% coverage with superficial checks. Coverage measures execution, not assertion quality. |
|
Make reviews concrete Reviewers can see exactly which code has tests, not just assumptions. |
Limited insight into dynamic behaviour Behaviour driven by runtime conditions, configuration, or environment variables may remain untested, even with high coverage. |
|
Focus testing strategically Coverage highlights high-risk, under-tested areas. |
Metric chasing distorts priorities When percentages become the goal, teams may write tests to satisfy metrics instead of uncovering real defects. |
|
Build confidence through measurement Track coverage trends to ensure changes are always tested and don’t introduce defects. |
|
|
Meet compliance requirements Coco supports audit‑ready reporting aligned with standards like ISO 26262 and DO‑178C. |
The bottom line: Use coverage to reveal blind spots and guide testing decisions but remember it only shows what executed, not whether your tests validated anything meaningful.
Making coverage work for your team
Three practical considerations:
-
Start with decision coverage as your foundation
For most teams, it exposes logic errors that line or statement coverage can miss without the complexity of MC/DC. Use it to find blind spots, not to “prove quality.” -
Align coverage depth with business risk
Payment processing, authentication, and data validation deserve rigorous coverage. UI formatting and logging typically warrant less. -
Make coverage visible in your workflow
Integrate coverage into CI, merge results across multiple test runs, track trends, set minimum thresholds, and investigate drops before merging. Ask:-
Which new execution paths are untested?
-
Do these gaps introduce unacceptable risk?
-
The most successful teams treat coverage as a diagnostic tool, one that raises better questions about risk and priorities rather than offering false reassurance.
Where Coco adds measurable value
If you rely on coverage, the tool’s capabilities matter. Coco goes beyond raw percentages to make coverage actionable, offering:
-
Coverage metrics from function and statement through decision, condition, MCC, and MC/DC
-
Merged coverage across multiple test runs and platforms
-
Clear identification of untested paths, redundant tests, and dead code
-
Test execution optimization when time or resources are limited
-
Per-test coverage, CI integration, and audit-ready reporting aligned with safety standards
-
Cross-language support (C/C++, C#, QML, Tcl) without requiring source code changes
With Coco, teams can see exactly what their tests are doing, and what they’re not, then act quickly to close the gaps.
Smart Testing Strategies: Unveiling Code Coverage Benefits
Our webinar on smart testing strategies demonstrates how leading teams use coverage analysis to guide their testing efforts, identify high-risk code, and build measurable quality into their development process without getting lost in metrics.
Watch the webinar to see these principles in action
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.
Even if you understand what 100% statement coverage means, it’s just one piece of the testing puzzle. To truly measure quality, you also need to consider test coverage and test effectiveness, and how all three metrics work together. In our blog, “Code Coverage vs. Test Coverage vs. Test Effectiveness: What do you measure?”, we break down each metric, where they overlap, and how to connect them into one actionable workflow.
Want to see what your current tests are missing?
Read more about Coco Code Coverage