
Stick to Your Guns: TDD When AI Writes the Code
You probably remember how hard it was to explain to the business why we needed to set aside a...
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You probably remember how hard it was to explain to the business why we needed to set aside a...
High-capacity, high-density flash memory such as BiCS FLASH (3D NAND) is prone to bit inversion due...
This is sample code for wear-leveling functionality for BiCS FLASH (3D NAND) controllers, along with...

ما الذي تدين به رسالة الفشل لقارئها؟ متى كانت آخر مرة قرأت فيها — فعلاً قرأت — رسالة فشل...

A test fails at 2 AM. You open the CI log, and the entire story of the failure is one...
Two tests can pass while applyDiscount(100) returns 100 instead of the ticket's 90. One replaces the...

High coverage, weak mutation score: why AI-written tests pass without catching bugs. How mutation testing works, real numbers, and how to run it in CI.

Intent defines the starting semantic. Trajectory defines the behavioral evolution. Destination...

Intent defines the starting semantic. Trajectory defines the behavioral evolution. Destination...
Example-based tests encode cases an agent can enumerate and hardcode past. Property-based tests quantify over inputs the agent did not see at code-write time, which is the gap that makes the lookup-table attack more expensive than the honest fix. Properties are one layer in the enforcement stack, not the whole stack.
In this article I describe three problems I ran into while using mocks: Lost...

The first seven posts were about mechanics. This one is a single real run: three features handed to pdlc's own loop engine, all three at the review terminal state in 88 minutes, one failed quality gate, then a release and a deploy to two machines. How to split the work, who does what, how the loop runs, how long it took, how much it produced, and what holds quality up, answered with numbers from the event log and from git.