code-quality

How to Effectively Write Quality Code with AI

by mia-heidenstedt

Developers who treat AI as an autonomous coder lose control of their codebase—effective AI collaboration requires explicit architectural decisions, layered verification systems, and human-written specification tests that resist shortcuts.

Detecting AI Slop with Distance from Main Sequence

Normalized Distance from Main Sequence (D) identifies structurally poor code by measuring how far components deviate from the ideal balance of abstractness and stability—making it the single best metric for detecting AI-generated code that compiles but rots.

Kill All Mutants! (Intro to Mutation Testing)

by dave-aronson

Code coverage tells you what code runs—mutation testing tells you if your tests would actually catch bugs by deliberately breaking code and checking whether tests fail.

Stryker Mutator

by simon-de-lang

Code coverage measures what runs, but mutation testing reveals whether your tests actually catch bugs by deliberately breaking code and checking if tests fail.

Mutation Testing Skill

by paul-hammond

Code coverage lies about test quality—mutation testing exposes weak tests by asking 'if I introduced a bug here, would my tests catch it?'

Refactoring: Improving the Design of Existing Code

by martin-fowler

The definitive guide to improving code structure without changing behavior. Fowler catalogs refactoring techniques with clear examples, making messy code maintainable.

Avoid Nesting When You're Testing

by kent-c-dodds

Nested describe blocks with beforeEach hooks create cognitive burden by requiring developers to track mutable state across scope levels—prefer inline setup or factory functions instead.