Coco is a code coverage tool for software teams developing desktop applications and embedded systems. Trusted by leading automotive tier-1 companies, Coco supports C, C++, C#, QML, Tcl and Python.
Coco is a code coverage tool for software teams developing desktop applications and embedded systems. It instruments source code at the compiler level and captures exactly which statements, branches, and conditions are executed during testing.
Coco integrates natively with Jenkins, GitHub Actions, and GitLab CI, outputting coverage data in Cobertura XML, JUnit, SonarQube XML, and EMMA-XML formats so that CI pipelines can enforce threshold gates. It supports C, C++, C#, QML, Tcl, and Python (via Coverage.py) across desktop, embedded Linux, and bare-metal MCU targets.
With Coco, teams can generate audit-ready reports aligned with international safety and compliance standards including automotive ISO 26262 and aerospace DO-178C/DO-330. Coco is independently certified by SGS TUV Saar (Certificate No. FS/71/220/26/2113) for safety-related software verification up to ASIL D, the highest automotive safety integrity level, confirmed by external audit.
Highlights
One Tool, Every Test: Measure coverage across your entire QA spectrum - from automated unit and integration tests to manual exploratory sessions and GUI-driven workflows. Coco captures every executed code path, regardless of the testing method, and merges them into a single, unified source of truth.
Coco covers C, C++, C#, QML, Tcl, and Python in a single instrumentation database. On embedded targets it collects coverage through GDB, serial, TCP/IP, CAN bus, or ARM semihosting including RTOS-based systems and bare-metal MCUs. Used widely in industries including automotive, aerospace, medical and industrial automation.
Coco's Patch Analysis maps every changed line against existing coverage data and identifies which tests executed those paths without re-running the full suite. The CRAP metric (Change Risk Anti-Patterns) combines cyclomatic complexity with coverage gaps to rank every function by risk, so engineers know exactly where one new test reduces the most exposure. Results are exportable for sprint planning, QA reviews, and certification evidence.
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Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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You buy Coco through a single dimension: a Process License sold in a pack of 5, priced per host. Licenses come in increments of 5, so you scale by adding packs as your team or number of hosts grows. Each license covers code coverage analysis on a host, whether you run it locally or cross-compile for embedded and RTOS targets. There are no separate tiers to choose between here. To increase capacity, you add more packs rather than upgrading to a different plan.
Top-of-mind questions for buyers
What counts as one host for a Process License in the pack of 5?
A host is a machine where you run code coverage analysis. This can be a local development machine or a system used to cross-compile for embedded and RTOS targets. Each pack covers 5 hosts. You track and license each host that runs the tool.
What happens when I need coverage on more hosts than my current packs cover?
Licenses come only in increments of 5, so you add another pack of 5 to gain more host capacity. There are no separate tiers or plan upgrades. You scale by buying additional packs as your team or number of hosts grows.
Does using Coco on embedded or RTOS targets change how licenses are counted?
No. Licensing counts hosts running the tool, not the targets you test. You can run coverage locally or cross-compile for embedded and RTOS targets under the same per-host license. The target hardware does not add license units beyond the host that runs the analysis.
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This product includes charges for expert support, configuration, and maintenance services. CVAT is an open-source computer vision annotation platform designed for efficient labeling of images and videos. Pre-configured for rapid deployment with automated workflows, and cloud storage integration, export high-quality datasets in YOLO and COCO format . Expert support, configuration, and maintenance services are available for seamless setup and ongoing operations.
COCO is a large-scale object detection, segmentation, and captioning dataset.
This is part of the fast.ai datasets collection hosted by AWS for convenience
of fast.ai students. If you use this dataset in your research please cite
arXiv:1405.0312 [cs.CV].
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