Keep Your Code SOLID in the Age of AI Copilots

AI tools like GitHub Copilot, Claude AI and ChatGPT are transforming how we write code. They save time by generating code snippets, functions, and even entire classes in seconds. But there’s a hidden challenge: these tools are pattern-driven, not principle-driven. They can churn out functional code quickly, but they lack the ability to align that code with essential design principles like SOLID—Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion.

The result? While AI copilots can accelerate development, they often create code that is rigid, overloaded, or tightly coupled, leading to technical debt down the road.

In this post, I’ll share how you can ensure that AI-generated code adheres to SOLID principles. By following a few practical strategies, you can combine the speed of AI with disciplined design practices to create clean, maintainable, and flexible software.

Why AI Copilots Struggle with SOLID Principles

AI copilots excel at recognizing patterns in training data. However, they lack the judgment to evaluate why certain code structures work better than others. This can lead to common violations of SOLID principles, including:

1. Responsibility Overload

AI-generated classes often take on too many tasks at once, violating the Single Responsibility Principle (SRP). For instance, a generated class might handle business logic, database operations, and API calls all in one place—making it harder to test and maintain.

2. Rigid Code That’s Hard to Change

The Open-Closed Principle (OCP) emphasizes that code should be open to extension but closed to modification. AI copilots, however, frequently generate code that’s not easily extensible, forcing developers to rewrite large sections when requirements change.

3. Tightly Coupled Dependencies

Copilot often creates classes with concrete dependencies (e.g., directly instantiating classes instead of relying on abstractions). This breaks the Dependency Inversion Principle (DIP) and makes the code less reusable and harder to test.

Practical Strategies for Keeping AI-Generated Code SOLID

To bridge the gap between AI speed and human judgment, you’ll need to actively refine and review the code AI generates. Here’s how:

1. Conduct Code Reviews with SOLID in Mind

Code reviews are your first line of defense against design violations.

• Check for Single Responsibility: Look for classes that try to do too much. For example, if a generated "OrderProcessor" class also sends emails and logs events, it’s a red flag. Split such responsibilities into separate classes.
• Look for Extensibility: Ensure the code can be extended without modifying existing logic. If a "DiscountCalculator" class is hardcoded for specific discount types, consider refactoring it to use a strategy pattern.
• Ensure Substitutability: Test that subclasses can replace their base classes without changing the expected behavior, adhering to the Liskov Substitution Principle (LSP).

2. Use Static Analysis Tools to Enforce SOLID Principles

Static analysis tools can help identify potential SOLID violations early.

• Spot Responsibility Overload: Tools like SonarQube flag large classes or methods that handle multiple concerns, helping you identify SRP violations.
• Catch Tight Coupling: Configure tools to warn against concrete dependencies. For example, if an AI-generated "ReportGenerator" class directly instantiates a "DatabaseConnector" class, replace it with an interface to respect DIP.

3. Write Tests to Validate SOLID Principles

Tests aren’t just for catching bugs; they can enforce design principles too.

• Unit Tests for SRP and ISP: Write tests that fail if a class takes on too many responsibilities or depends on unnecessary interfaces. For example, a test should fail if a "UserService" class depends on an "EmailSender" it doesn’t use.
• Integration Tests for Substitutability: Test that a derived class can seamlessly replace its base class in existing workflows without causing errors.

4. Schedule Regular Refactoring of AI-Generated Code

AI-generated code often needs cleaning up to align with best practices.

• Refactor Often: Set aside time to split large classes, improve readability, and remove unnecessary dependencies. For example, break up a monolithic "BookingManager" class into separate components like "PaymentProcessor" and "NotificationSender."
• Audit Dependencies: Replace direct dependencies with abstractions wherever possible. Use design patterns like dependency injection to make your codebase more flexible and testable.

Conclusion

AI copilots can supercharge development, but they aren’t perfect. They generate code that works but often doesn’t adhere to critical design principles like SOLID. That’s where you come in.

By conducting thoughtful code reviews, leveraging static analysis tools, writing targeted tests, and scheduling regular refactoring, you can ensure AI-generated code stays clean, maintainable, and aligned with best practices.

AI copilots may assist in writing code, but the responsibility for writing the right kind of code—code that’s flexible, scalable, and SOLID—still rests with you.