A Docker image providing a comprehensive suite of static analysis and quality assurance tools for PHP projects.
phpqa is a Docker image that packages a wide array of static analysis and quality assurance tools for PHP into a single container. It solves the problem of manually installing and configuring dozens of individual QA tools by providing a ready-to-use environment that can be run locally or in CI/CD pipelines. The image includes tools for linting, coding standards, architecture testing, security auditing, and performance benchmarking.
PHP developers and teams who want to integrate comprehensive code quality checks into their development workflow without the overhead of managing tool installations. It's particularly valuable for projects implementing CI/CD, enforcing coding standards, or maintaining large codebases.
Developers choose phpqa because it offers a consistent, versioned environment with all major PHP QA tools pre-installed, eliminating dependency conflicts and setup time. Its Docker-based approach ensures tools run identically across all environments, from local machines to production CI servers.
Docker image that provides static analysis tools for PHP
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Bundles over 80 pre-configured tools like PHPStan, Psalm, and Rector, eliminating the need to manage individual installations and reducing setup time.
Supports PHP 7.1 through 8.5 with Debian and Alpine variants, making it versatile for legacy and modern projects, as shown in the extensive tag list.
Designed for seamless pipeline integration with simple Docker commands, ensuring consistent tooling across development and production environments.
Tags are updated daily or on patch changes, keeping tools current and secure, as indicated in the README's versioning strategy.
Entirely reliant on Docker, making it unusable in environments where containerization is not feasible, such as systems without Docker support.
Some tools are missing or have limited support; for example, Codeception is unavailable for PHP 8.5, and exakat is excluded, requiring separate management.
Requires volume mounting and temporary directory management, as noted in the running instructions, which can be cumbersome for quick local use.
Running tools in Docker containers adds latency compared to native installations, potentially slowing down feedback loops in development.