Programming as Theory Building: Developing a Mental Model of Software

The act of programming is often reduced to the mechanical task of writing lines of code or implementing specific features. However, a more profound way to view the software development process is as an exercise in "theory building." Instead of seeing a program as a static set of instructions, this approach suggests that reading and understanding code is the process of constructing a mental model—a theory—of how the system behaves, why it exists, and how its components interact.

This shift in perspective is critical for engineers who want to move beyond myopic feature-building and toward the creation of cohesive, maintainable systems. When we read code, we aren't just parsing syntax; we are attempting to derive a general rule or a conceptual framework that explains the observed behavior of the software.

The Core Concept: Theory Building vs. Feature Implementation

Many developers fall into the trap of focusing solely on individual features. As noted by community members, this leads to a lack of cohesiveness in the software. When engineers focus only on the "how" of a specific task, they often ignore the broader interfaces and contracts that hold the system together.

Theory building is the antidote to this myopic view. It involves asking not just "how does this function work?" but "what is the theory of this program?" This means understanding the underlying assumptions, the architectural patterns, and the invariants that the code is intended to uphold. Without this theory, software becomes a collection of disjointed patches rather than a unified architectural vision.

The Role of Decomposition and Representation

While some may find the term "theory building" abstract, the practical application of this concept manifests as effective design. High-quality programming is fundamentally about decomposition and representation. The goal is to find an ideal factorization of the problem—reducing requirements into a design with the fewest moving parts and the simplest interfaces.

In this framework, the "theory" of a program is the mental model of its decomposition. A successful implementation is one where the code reads like a high-level description of the requirements, with complexity hidden behind layers of abstraction. The process of programming then becomes a co-evolution of the requirements and the mental model used to represent them.

Programming in the Age of LLMs

The rise of Large Language Models (LLMs) has introduced a significant tension into the theory-building process. AI can generate code rapidly, but it does not possess a "theory" of the program. It operates on patterns and probabilities rather than a conceptual understanding of the system's architecture.

This creates several risks for the modern developer:

• Loss of Conceptualization: When developers rely too heavily on AI-generated code, they may bypass the micro-architectural decisions that normally force them to build a mental model. This leads to a "vibe shift" where the developer is no longer the primary architect of the theory.
• Accumulation of Unknowns: There is a growing trend of accumulating code that is written by AI but not fully understood or read by the human engineer. This creates a dangerous gap between the actual behavior of the system and the human's mental model of it.
• The New Role of the Engineer: As AI handles more of the boilerplate and bug-fixing, the engineer's primary value shifts toward the collection and retention of context. The human engineer must now act as the guardian of the program's theory, ensuring that the AI's contributions align with the overall architectural vision.

Strengthening the Theory with Automated Tests

If the theory of a program is a mental model, then automated tests are the executable documentation of that theory. Tests serve as a powerful tool for theory building by forcing the developer to explicitly define the expected behavior of the system.

To maximize the value of tests in theory building, certain practices are recommended:

• Red-Green Testing: Ensuring a test fails before it passes verifies that the test is actually testing the desired behavior.
• Focus on Behavior, Not Implementation: Tests should verify obscure behaviors, edge cases, and the results of critical bug fixes to prevent regressions.
• Avoiding I/O and Third-Party Dependencies: Keeping tests fast allows them to be run frequently, reinforcing the mental model through rapid feedback loops.

By treating tests as a way to document the "why" and the "how" of a system, engineers can provide a reliable source of truth that both humans and AI agents can use to understand the program's intended theory.