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Coco Code Coverage — Product Overview [Document]
Most teams know their test coverage percentage. Far fewer know whether the code they haven't tested is safe to leave that way. The difference between those two things is where production defects live, where certification audits stall, and where release confidence breaks down. Coco is built to close that gap.
Using compiler-level instrumentation, Coco captures real execution data across C, C++, C#, QML, Python via coverage.py, and Tcl on everything from desktop applications to safety-critical embedded systems, and turns it into something every role on your team can act on immediately. The result is faster test cycles, more confident release decisions, and measurable evidence of software quality that holds up in audits and reviews.
Stop guessing where your testing effort should go
Coverage percentages tell you how much code was exercised. They don't tell you whether the untested parts matter. Coco's CRAP (Change Risk Anti-Patterns) metric changes that by combining complexity analysis with coverage data to produce a risk score for every function in your codebase. The functions that are both complex and undertested rise to the top — giving developers and QA leads a ranked, prioritized list of exactly where a new test will reduce the most risk. Teams that previously spent time debating where to focus testing effort now work from evidence instead.
Release with confidence in regulated industries
For teams working under functional safety standards, Coco removes two of the most time-consuming parts of the certification process. Its TÜV Saar assessment means the tool itself arrives pre-validated, eliminating the need to build qualification evidence from scratch. Its per-standard qualification kits for ISO 26262, DO-178C, IEC 62304, and EN 50128 provide the documentation, pre-built test cases, and safety manuals needed to satisfy auditors — so compliance teams spend less time preparing evidence and more time on the work that matters.
Get a complete picture across your entire codebase
Mixed-language teams no longer need to maintain separate coverage workflows for different parts of their stack. Python coverage from coverage.py imports directly into Coco and merges with C/C++ results into a single unified view — so nothing falls outside the picture and no gaps are hidden by tool boundaries. For embedded teams, DeviceLink integration retrieves coverage data directly from Qt for MCUs target boards without debuggers or custom tooling, cutting the time and friction involved in getting coverage data off the device.
Fits the way your team already works
Coco integrates with Jenkins, GitHub, GitLab, and SonarQube and works with all major build systems and test frameworks without requiring changes to how you build or test today. Coverage data feeds quality gates, blocks merges below defined thresholds, and flows into team dashboards automatically — giving test managers and team leads the visibility they need without adding process overhead.
What's inside this overview
- How the CRAP metric turns coverage gaps into a ranked action list — and why coverage percentages alone are not enough to manage software risk
- How teams in automotive, aerospace, industrial, and medical software use Coco to satisfy ISO 26262, DO-178C, IEC 62304, and EN 50128 requirements with less effort
- Full coverage level support from statement and branch through to MC/DC and MCC — the criteria required by the most demanding safety standards
- How Python and C/C++ coverage merges into a single view for mixed-language teams
- Qt for MCUs DeviceLink integration and what it means for embedded coverage workflows
- CI/CD integration, audit-ready reporting, and SonarQube-compatible export
Download the overview to see the full capability set, and find out what your coverage percentage has not been telling you.
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