</> htmx ~ Codin' Dirty

Test-Driven Approaches to Software Engineering with Large Language...

Tests increasingly participate in the decisions made by large language models and software engineering agents. They specify intended behavior, guide program construction and repair, select...

Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study

As autonomous coding agents see rapid adoption, their evaluation has primarily focused on task completion rates holding the target codebase fixed. This leaves a critical question unanswered: does the structural and stylistic quality, or ``cleanliness'' of the underlying code affect an agent's ability to navigate and modify it? To isolate the effect of code cleanliness from agent capability, we introduce an evaluation protocol built around minimal pairs: repositories that match on architecture, dependencies, and external behaviour, but differ on static-analysis rule violations and cognitive complexity. The pairs are constructed in both directions, by agent pipelines that either degrade a clean repository or clean a messy one. We author 33 tasks across six such pairs, evaluated through hidden tests at the application's public surface. Across 660 trials with Claude Code, code cleanliness does not change the agent's pass rate. However, it substantially alters the agent's operational footprint: agents working on cleaner code use 7 to 8% fewer tokens and reduce file revisitations by 34%. Our findings suggest that traditional maintainability principles remain highly relevant in the era of AI-driven development, shaping the computational cost and navigational efficiency of coding agents. Code cleanliness joins model choice, harness, and prompting as a factor that materially affects agent behaviours.

Write code that is easy to delete, not easy to extend.

“Every line of code is written without reason, maintained out of weakness, and deleted by chance” Jean-Paul Sartre’s Programming in ANSI C.

Antithesis: autonomous software testing

Try the Antithesis autonomous testing platform and find bugs in your software with perfect reproducibility

Improving Science That Uses Code

Abstract. As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being

How to create software quality.

I’ve been reading Steven Sinofsky’s Hardcore Software, and particularly enjoyed this quote from a memo discussed in the Zero Defects chapter: You can improve the quality of your code, and if you do, the rewards for yourself and for Microsoft will be immense. The hardest part is to decide that you want to write perfect code. If I wrote that in an internal memo, I imagine the engineering team would mutiny, but software quality is certainly an interesting topic where I continue to refine my thinking. There are so many software quality playbooks out there, and I increasingly believe that all these playbooks work in their intended context, but are often misapplied.

Testing can be fun, actually

Writing and maintaining tests is boring. But they're also some of the most valuable code we can write. With this blog post you'll learn a criminally underrated testing technique to add to your testing toolbox that can make tests a whole lot more pleasant.

The value of human, exploratory testing

Ann and Michael find things programmers never would have.Since unit testing and test-driven development burst onto the programming scene in the early 2000s, too many programmers have deluded themse…

/code

/code in Google Labs contains various experiments for code and software development

R. S. Doiel, Software Engineer/Analyst — Robert's ramblings

By R. S. Doiel, 2026-02-21 (revised: 2026-03-03, epilogue added 2026-03-27)

Diving into Claude Code's Source Code Leak

Engineer’s Codex is a publication about real-world software engineering.

The Bulk of Software Engineering is Just Plumbing

I read a post online recently about how some software engineer was asked to complete a technically complex, jargon-filled coding challenge over the...

Modeling Software With Quint - Zicklag's Leaflets

I believe having good abstractions is key to writing good code. But as a coder, I often write code in an effort to find those abstractions…

Ephemeral testing

We have many ways to ensure software quality. Unit testing. Fuzz testing. Integration testing. And so forth.   I'd like to propose a method that was unthinkable before: ephemeral testing. (Ephemeral is a fancy word for 'throw away' or 'temporary'.)   You write your code. You build your software component. Or the AI agent does … Continue reading Ephemeral testing

The End of Code Review: Coding Agents Supersede Human Inspection

Code review has been the primary quality gate in software development since Fagan formalised code inspection in 1976. For five decades, having a human examine and comment on a colleague's changes before merge has been a cornerstone practice at organisations of every size. Coding agents are large language model (LLM)-based autonomous systems capable of reading, writing, testing, and repairing software. We argue that coding agents have crossed a threshold of capability at which traditional human code review is no longer a necessary component of a software quality pipeline. Our argument rests on two claims: every stated goal of code review can be served by agents at lower cost and higher throughput; the naive integration in which agents write code and humans remain the mandatory reviewers is a dead end because it neither provides meaningful assurance nor scales with AI-assisted throughput.