End-to-End Code Coverage for Embedded Solutions

In safety-critical development, it’s not just about how much code is covered but which parts are tested and how thoroughly. Standards like ISO 26262, DO-178C, and IEC 61508 require more than just line or branch coverage, they demand Modified Condition/Decision Coverage (MC/DC) and traceability.

Coco supports all key coverage levels, from basic function and line coverage to condition, decision, and MC/DC. It visually maps these coverage results in its CoverageBrowser, helping QA teams pinpoint untested logic and verify that tests align with both technical and regulatory expectations.

Full regression suites can take hours, or even days. And running everything after every change is rarely efficient.

Coco introduces change-based testing. Its patch analysis feature automatically compares source code revisions and tells you which tests are needed based on the changes. This means your team can avoid redundant test executions, reduce load on CI infrastructure, and shorten test feedback loops, all without compromising coverage.

Emulators are useful, but they don’t fully replicate runtime behaviour on real hardware, especially in embedded systems.

With Coco’s runtime instrumentation, you can run tests on actual embedded targets (including RTOS-based systems) and collect reliable coverage data. This ensures your metrics reflect how the software behaves in production environments, not just in simulation.

Many teams are now distributed across offices, time zones, or even external partners, and not everyone can (or should) access source code.

Coco enables black-box testing and secure distributed workflows. Its architecture separates code instrumentation from test execution, so you can generate instrumented binaries locally and share them with outsourced or remote QA teams. Test data can be collected without sharing source code, then sent back and merged into centralized coverage reports.

A high overall percentage can feel reassuring but it doesn’t mean critical logic is being tested. Numbers alone don’t reveal redundant tests, gaps in decision logic, or coverage volatility over time.

Coco goes beyond percentages. With features like CoverageBrowser visualizations, test impact analysis, and report merging, teams gain actionable insights, not just data. You can track coverage across versions, see how tests relate to code structure, and understand how changes affect coverage over time.

With the Test Center add-on, you can also track coverage trends over time and manage results across projects.

Read more in the blog: Is 70%, 80%, 90%, or 100% Code Coverage Good Enough? What 100% Statement Coverage Really Tells You and What It Doesn’t 

Many coverage tools break or produce incorrect results when dealing with real-world systems that mix C++, C#, QML, Tcl, or SystemC. Some don’t support embedded compilers or non-standard toolchains.

Coco was designed with cross-language, cross-platform development in mind. It supports a broad set of languages and compiler toolchains, including embedded environments and safety-related workflows. It integrates easily with Qt-based projects, making it uniquely suited for teams developing with Qt/C++ and QML.

Legacy systems often have limited documentation and little test coverage. Understanding them well enough to build meaningful tests can be a major challenge.

Coco helps you explore legacy code from a test perspective. It identifies untested branches, functions, and conditions, giving you a clear roadmap for where to focus your testing efforts. Instead of rewriting the code, Coco helps you write better tests, increasing safety and stability without starting over.