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Expert articles, deep dives and current insights from the world of AI development, SaaS and digital transformation.
Definition of Done for AI Code: Evidence Instead of Promises
When code is written in minutes, the bottleneck shifts to the question of when something is actually done. A Definition of Done built for that pace.
Code Audit: A Checklist for Reviewing Someone Else's Software
Before a handover, before a purchase, after an incident: what a code audit checks, in what order, and how to spot a superficial one.
Test Coverage for AI Generated Code: Why the Percentage Is Misleading
AI writes tests fast, and derives its expectations from the very code it is supposed to check. Here is how to avoid that trap.
Quality Gates in the CI/CD Pipeline: Building Four Stages
A pipeline that only builds and ships is a conveyor belt. Here is how it becomes a checkpoint chain that stays fast and still knows when to stop.
What Is a Quality Gate? Definition, Criteria, and Common Mistakes
A quality gate is a barrier with fixed criteria that a change must pass. How to build one that actually helps instead of just being annoying.
Clean Code in the AI Era: Which Principles Stay and Which Shift
When a machine writes the code, readability does not become less important, it becomes more important. Which of the old rules count now.
Modernizing Legacy Code With AI: What Works and What Goes Wrong
AI can explain, document, and test old code, that is the underrated win. The traps sit in the rewriting.
Refactoring AI-Generated Code: An Approach That Breaks Nothing
Refactoring has almost vanished from repositories, from 21 down to 3.8 percent. How to restructure generated code without carrying its bugs along.
Identifying and Fixing Code Smells: The Ten Most Common in AI Code
A code smell is not a bug, it is a signal. Which patterns generated code reliably produces, and how to resolve each one.
Static Code Analysis: What It Finds, What It Misses, and Which Tools Fit
Static analysis checks code without running it: the cheapest error brake there is. Where its limits lie and how to introduce it into an existing codebase.
Code Review for AI Code: Six Patterns the Classic Review Misses
The classic review looks for careless mistakes. AI code has a different kind: it looks correct. A review workflow built for exactly this class of error.
Measuring Code Quality: Which Metrics Matter and Which Just Reassure
Test coverage alone says almost nothing. Which metrics give a real read on maintainability, and how ISO 25010 helps make sense of it.
All images in this overview are AI-generated illustrations.AI transparency
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