Ephemeral testing

Antithesis: autonomous software testing

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

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...

A universal approach to mocking

Defunctionalise your continuations and your tests can run any computation a step at a time.

How Antithesis Turned exe into a Sandbox for Agentic Software Tests - exe.dev blog

Carl Sverre spends a lot of time thinking about how to give AI agents the right amount of power. Give them too little, and they can’t do real work. Give them too much, and they might blow up your tech stack. As a software engineer at Antithesis, an autonomous software testing platform, that question is core to how Sverre thinks about designing tools in the era of advanced AI.

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…

</> htmx ~ Codin' Dirty

In this article, Carson Gross discusses an alternative approach to software development that challenges the principles outlined in 'Clean Code.' Carson advocates for allowing larger functions in certain cases, preferring integration tests over unit tests, and minimizing the number of classes and interfaces. He shares examples from successful software projects that demonstrate these practices can lead to maintainable, high-quality code.

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.

A new era for software testing - <antirez>

Enosis Solutions - Software Development and Testing Services

We design, build, test, modernize, and scale software across the entire product lifecycle, combining deep technical expertise with an AI-accelerated build process.

AI Code Review | Greptile | Merge 4X Faster, Catch 3X More Bugs

AI Code Reviews that understand your entire codebase. Automate PR reviews, catch bugs faster, improve code quality with AI-driven analysis. Try Greptile free!

Tests Are The New Moat | Daniel Saewitz

As AI becomes better at cloning people's open source work, what ends up becoming most valuable are software contracts, tests, and API surface area. This clashes the incentives of clearly defining your commercialized open source software with protecting it.

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.

Agentic test processes, LLM benchmarks, and other notes on agentic coding from Galapagos Island

I've been using AI fairly heavily since last November and the whole thing is a funny experience. An agent will do something that, if a human did it, you'd immediately fire them. My reaction, of course, is to act as if this is great and spin up a thousand agents so they can do even more of that.

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.

Property Testing with Agent Swarms

Using agents to build property-testing machinery, then turn the findings into small, reviewable fixes.

Readable Robot Tests: Bridge the Tech Gap

A walkthrough of the Robot Testing pattern in Flutter, an end-to-end technique that keeps tests readable for non-technical stakeholders and reusable as code grows.