Compression-Oriented Programming: A Practical Approach to Abstraction

Casey Muratori’s blog post, Semantic Compression, presents a compelling alternative to traditional object-oriented programming (OOP) practices. By optimizing the UI code for The Witness, the author demonstrates how step-by-step code compression can lead to improved readability, maintainability, and scalability. This article distills the core ideas and methodology outlined in the post.

The Concept of Code Compression

  • Code compression programming refers to simplifying code to its minimal expression by extracting repetitive patterns, reducing redundancy, and increasing the level of abstraction.
  • This approach starts with detailed optimization and avoids relying on pre-planned architecture. Instead, the final system architecture emerges naturally through iterative compression and optimization.
  • Unlike the traditional top-down “design architecture first” approach in OOP, code compression minimizes the risk of overlooking details or creating suboptimal solutions during architectural design.

Optimization Starting from Details

  • The author extracts patterns from repetitive code, such as button calculation logic and row management in UI layouts.
  • Local variables are abstracted into shared structures (e.g., Panel_Layout) to enable data sharing and simplify logic.
  • Each optimization step is intuitive and does not require complex tools or workflows (e.g., UML diagrams or index cards).

Step-by-Step Code Compression

  • Step 1: Establish a Shared Stack Framework
    Extract variables related to panel layout (e.g., row_height and at_y) into the Panel_Layout structure, acting as a shared state manager.

  • Step 2: Extract Repetitive Logic
    Encapsulate common operations (e.g., drawing window titles, creating rows, rendering buttons) into methods like window_title() and row(), reducing redundancy.

  • Step 3: Dynamic Computation
    Use dynamic computation to determine layout heights (via the complete() method), avoiding hardcoded declarations and enhancing code robustness.

  • Step 4: Further Logic Compression
    Simplify frequent calls (e.g., button rendering) into more concise methods like push_button(), making the code cleaner and more compact.

Through these steps, the code transforms from a complex, verbose structure into a streamlined form containing only the essential logic.

Reflections on Object-Oriented Programming (OOP)

  • Criticism of Traditional OOP Methods: The author argues that traditional OOP methods overemphasize pre-designed architectures, making them overly complicated and inefficient.
  • Code Is Not Intrinsically Object-Oriented: Code is fundamentally procedural, and objects naturally emerge as structures to facilitate program reuse.
  • How to Derive the Right Objects: Objects should not be forcibly designed in advance but should naturally emerge through code compression and refinement.

Broader Insights

  • The Key to Program Design is to understand and address detailed problems first, then extract general patterns through code compression, leading to natural abstractions and architectures.
  • Avoid Premature Complexity: Resist the urge to plan an “ideal” system prematurely. Instead, iteratively optimize simple code to evolve into a practical and functional structure.

Summary

This article advocates for a programming approach focused on starting with details and achieving architectural optimization through code compression. It opposes the traditional OOP method of “designing the architecture first.” This approach makes the code more intuitive and efficient while avoiding potential pitfalls in architectural design.