The Concept of Clean Code for JavaScript: Empirical Boundaries and Optimization-Aware Design

This preprint presents an optimization-aware interpretation of clean code for JavaScript. It argues that clean JavaScript code should be understood not only as readable and visually simple code, but also as code whose data structures, allocation behavior, normalization boundaries, and hot-path mechanics remain sympathetic to the runtime. The empirical part of the article reports a local benchmark study of 24 benchmark groups and 62 implementation cases executed with Node.js v23.9.0, V8 12.9, on an Apple M3 environment. The study compares common JavaScript implementation choices related to iteration style, membership lookup, object shape stability, array density, allocation patterns, error control flow, date parsing, regular expression preparation, copying strategies, internationalization object reuse, memoization, validation, rendering scope, and URL query construction. The conceptual part proposes a view of clean code as executable communication with three obligations: semantic clarity for the reader, mechanical sympathy for the runtime, and evolutionary honesty for future change. The article introduces practical principles such as boundary normalization, shape respect, allocation honesty, measured restraint, scope exactness, reusable interpreters, targeted immutability, and evidence-tagged hot paths. The benchmark results are local and should not be treated as universal performance laws. They are intended as empirical motivation for design rules and as a practical reminder that readability and runtime behavior should be considered together in performance-sensitive JavaScript code.