Skip to main content

Why Faster Code Isn't Always Better Code

· 3 min read
Dr. Nicholas Knize
Dr. Nicholas Knize
Co-founder & CTO

Auto-generated code is often marketed as a time-saver: fewer keystrokes, less boilerplate, and cleaner files. But beneath the surface, this convenience can quietly introduce performance bottlenecks, security vulnerabilities, and unpredictable behaviors in production systems.

What Is Auto-Generated Code?​

Auto-generated code refers to software written by tools, libraries, or annotations rather than developers themselves. Common examples include Lombok annotations like @Data, codegen frameworks, and even AI-assisted tooling like GitHub Copilot. While these approaches reduce repetition, they also obscure what the system is actually doing.

The Hidden Pitfalls​

To be clear, auto-generated code isn't inherently bad. When used with intention—such as reducing repetitive getter/setter methods, scaffolding REST endpoints, or rapidly prototyping utility classes—it can drastically improve developer efficiency. The key is to understand what's being generated, audit it for correctness, and avoid using it in areas where performance, caching, or security are mission-critical.

Let's look at a seemingly innocent example:

@Data
public class User {
  private String id;
  private String name;
  private int age;
}

At a glance, this class looks great—all the boilerplate is handled for you. But in high-performance systems, this simplicity becomes a liability.

Cache Misses​

Hash-based collections like HashMap and HashSet rely on consistent hashCodes. When mutable fields like name or age are included in autogenerated equals() and hashCode() methods, any change to these fields causes the object to become "invisible" to the collection, leading to silent cache misses.

Performance Hits​

Automatically generated methods often include unnecessary fields in their calculations. In large-scale systems, bloated hash logic translates into measurable performance degradation.

Debugging Nightmares​

When things go wrong, good luck tracking it down. Auto-generated logic is opaque by design. Developers often spend days debugging issues rooted in hidden logic they didn't write or even know existed.

Security Vulnerabilities​

Annotations like @Data may generate toString() methods that log sensitive data, exposing credentials or personal information in logs. Improperly generated equals() or hashCode() methods can also disrupt authorization checks or violate security constraints.

The Lucenia Approach​

At Lucenia, we recognize that code generation has its place—but only when handled intentionally.

  • Optimized Code Generation: AI models suggest boilerplate reductions only where they don't compromise correctness.
  • Customizable Hashing: Developers define which fields matter for identity and caching, avoiding silent failures.
  • Smart Caching Tools: The system identifies mutable risks and suggests alternative strategies to avoid cache breakage.
  • Traceable Debugging: Tools make generated logic transparent and easy to diagnose.
  • Secure-by-Design Defaults: Every output is scanned against OWASP and CWE best practices to avoid security violations.

Final Thought​

Code generation doesn't eliminate complexity—it hides it. In large systems, hidden complexity turns into real-world failure. At Lucenia, we believe in automation with intention: fast when it should be, careful when it must be.

Up Next: Vibe coding—AI tools generating code from loose prompts—is rapidly gaining traction. But what happens when AI gets it wrong? In our next post, we'll examine the risks of probabilistic programming and how to avoid building brittle systems around tools that guess. In large systems, hidden complexity turns into real-world failure. At Lucenia, we believe in automation with intention: fast when it should be, careful when it must be.