Code Quality in the AI Age

Exploring the fact-based realities of AI-assisted coding

AI-Asssisted Coding: The promises

Uncovering the AI productivity claim

Human motivation: are we outsourcing the fun and adding to the mundane

Are we focusing AI on the right problem

No need to settle at 55% faster, 10x ourselves

In a follow-up to 'Code Red' study, looked at on-boarding cost

The big win: optimise for software maintenance

  1. It's not a refactoring unless we improve the design - Need a gold standard to judge if we did or not

  2. It's not a refactoring if we fail to preserve the behaviour of the original code, e.g. we introduce a bug

Research: AI refactoring

  1. Module/class level smells e.g. low cohesion, God classes
  1. Function level smells e.g. copy-pasted logic, God functions, primitive obsession

  2. Implementation smells e.g. deep nested logic, complex conditionals

Can AI help us refactor existing code

Fact checking the AI refactorings: Can we separate the good from the bad refactorings

Outcome: elevated to the level of human experts with a fact-checking model

References