brooks-lint offers a comprehensive approach to AI-driven code reviews, leveraging insights from twelve revered engineering texts to assess and improve code quality effectively. This tool transcends traditional metrics like line count and complexity by providing in-depth diagnostics of six decay risk dimensions both in production code and test suites, complete with actionable insights and references to classic literature. Featuring clear severity labels, structured findings, and a health score, brooks-lint ensures that users receive not only analyses but also specific remedies to enhance maintainability and performance.

Key Features

• Depth of Analysis: Evaluate code against six distinct decay risks, highlighting potential issues before they escalate. These risks are derived from:
  • 🧠 Cognitive Overload: Assess the mental effort required to understand the code.
  • 🔗 Change Propagation: Determine how changes can inadvertently affect unrelated parts of the code.
  • 📋 Knowledge Duplication: Identify redundancies in decision-making across various code sections.
  • 🌀 Accidental Complexity: Uncover instances where the code is excessively complicated compared to the problem it addresses.
  • 🏗️ Dependency Disorder: Examine if dependencies adhere to a coherent flow.
  • 🗺️ Domain Model Distortion: Ensure the code accurately reflects the underlying domain model.

Example Insights

Given a code snippet for updating user profiles:

class UserService:
    def update_profile(self, user_id, name, email, avatar_url):
        user = self.db.query(f"SELECT * FROM users WHERE id = {user_id}")
        user['email'] = email
        ...
        if user['email'] != email:   # always False — silent bug
            self.smtp.send(...)
        points = user['login_count'] * 10 + 500
        self.db.execute(f"UPDATE loyalty SET points={points} WHERE user_id={user_id}")

brooks-lint provides a comprehensive report that includes:

• Health Score: 28/100
• Findings: The analysis indicates that this method bundles multiple responsibilities and includes a silent logic bug preventing email notifications.
• Remedy Suggestions: Recommendations to extract distinct services for better code organization and to properly handle email changes are provided.

Architectural Insights

In addition to code reviews, brooks-lint can deliver architecture audits via generated dependency graphs, allowing developers to visualize module interactions and pinpoint potential architectural flaws.

Continuous Improvement

With comprehensive capabilities that cover PR reviews, tech debt assessments, test quality reviews, and health dashboards, brooks-lint aims to integrate seamlessly into modern development workflows, making architectural integrity and code maintainability a priority.

Learn More

For further exploration of decay risks, visit the Decay Risk Field Guide or check out the Full Gallery showcasing brooks-lint outputs across various programming languages.

Join the movement to ensure robust, high-quality code that stands the test of time.