Why Code Becomes Unreadable Over Time and How to Keep It Simple

In the early hours before a deadline, the author discovers a bug in previously clear and elegant code, prompting a reflection on why code that once seemed understandable becomes confusing after weeks or months.

Problem 1 – Over‑complex mental models: Understanding why code becomes hard to read starts with recognizing how we construct mental models of the problem we aim to solve. Before coding, we must build a clear problem model, then a semantic model for the solution, and avoid mixing intent with implementation.

Creating the simplest possible semantic model helps keep the original intent visible; unnecessary complexity should be eliminated to ease future maintenance.

Problem 2 – Poor translation from semantic to syntactic model: Even with a good semantic model, a bad syntactic translation (the actual code) makes later comprehension painful. Preserving clues that link code back to the original semantic concepts (e.g., meaningful variable names) eases future revisions.

Practical advice:

Class structure and naming: follow Domain‑Driven Design principles to make classes self‑descriptive.

Variable, parameter, and method naming: use expressive names (e.g., paySalesCommission instead of process , currentContract instead of x ).

Single Responsibility Principle (SRP): each class or method should perform one well‑defined function, enabling clearer naming.

Comments: use them sparingly to explain *why* something is done, not *how*.

Problem 3 – Insufficient chunks: Reusing design patterns, algorithms, and functional‑programming primitives creates “chunks” that help bridge the gap between mental and code models, making it easier to reconstruct intent later.

Problem 4 – Confusing usage: Dispersed usage patterns make it hard to infer intended behavior; comprehensive test suites act as breadcrumbs that reveal how code should be used.

Problem 5 – No clear path between models: The transition from intent to semantic model to code should be smooth; choose structures and algorithms that naturally connect these layers.

Problem 6 – Inventing algorithms: Most problems can be solved by composing existing algorithms (e.g., shortest‑path, string similarity, particle swarm); reinventing them is rarely necessary.

Conclusion: Aim to build the simplest possible semantic model, translate it to code while leaving abundant clues, and use good naming, DDD, SRP, testing, and existing algorithms so that anyone revisiting the code can reconstruct the original mental model as easily as following a trail of breadcrumbs.