---
id: fastcov
version: "1.17"
license: mit
license_treatment: permissive
maintenance: active
---

# fastcov — A massively parallel gcov wrapper for generating intermediate coverage formats fast

License: permissive · Maintenance: active · Downloads: 117.4K/mo

## What it is and what it does

Fastcov is a Python tool that wraps gcov to generate code coverage reports in multiple formats—JSON, LCOV info, and SonarQube XML. It parallelizes gcov execution across multiple cores to process large numbers of .gcda files much faster than traditional tools like lcov. The tool is designed as a drop-in replacement for lcov in C/C++ projects, handling the intermediate coverage format generation that can then be consumed by report generators like genhtml or uploaded to services like Codecov and Coveralls.

The package requires GCC 9.0.0 or later (for JSON intermediate format and stdout streaming support) and Python 3.8 minimum. Object files must be compiled with absolute include paths or from the same root directory. Fastcov supports filtering by file path using substring matching, branch coverage selection, combining multiple coverage reports, and exclusion marker scanning. It ships as a single-file Python script that can be copied directly or installed via pip, with optional utility scripts for summarizing coverage and converting to SonarQube format.

Use it for:

- Accelerate CI/CD coverage report generation in large C/C++ projects with hundreds of .gcda files.
- Combine coverage reports from multiple test runs or build configurations into a single unified report.
- Generate coverage reports in SonarQube XML format for integration with SonarQube quality gates.
- Filter coverage data to exclude system headers and test files before publishing to coverage services.
- Check coverage thresholds (function, line, branch) in CI pipelines using the fastcov_summary utility.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Fastcov generates code coverage reports in JSON, LCOV, and SonarQube XML formats by parallelizing gcov execution, designed as a faster alternative to lcov for C/C++ projects.

Yes. Fastcov is actively maintained, has no dependencies, installs easily, and solves a real performance problem for C/C++ projects generating coverage reports. The MIT license is permissive. The only real constraint is the GCC 9.0.0 requirement—if your project meets that and uses gcov-based coverage, fastcov is a straightforward win for CI speed.

## Install

pip install fastcov
uv add fastcov
poetry add fastcov

## Installing fastcov

Before you install: Low friction: pure Python wheel with no runtime dependencies. Actively maintained as of 2026-07-15 with stable production status. Requires GCC 9.0.0 or later and Python 3.8 minimum.

License in practice: MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.

Quickstart:

pip install fastcov
fastcov.py --gcov gcov-9 --exclude /usr/include --lcov -o report.info
genhtml -o coverage_report report.info

GCC version 9.0.0 or later required; object files must be compiled with absolute include paths or from the same root directory; Python 3.8 minimum.

Verify before relying:

- Whether fastcov's parallelization speedup (claimed as 100x faster than lcov) holds across different project sizes and core counts.
- Compatibility with non-GCC compilers (e.g., Clang) for coverage generation.
- Whether the Docker image is the recommended deployment method or if native installation is equally supported.

## Package facts

- License: mit (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 117.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

gcov coverage report generator, parallel code coverage processing, lcov replacement tool, C++ coverage analysis, fast coverage format conversion, gcda to coverage report, coverage report aggregation, code-coverage, c-cpp-testing, ci-cd

[View on SkillFed](https://skillfed.io/packages/fastcov) · [View on PyPI](https://pypi.org/project/fastcov/)