Six Principles of Automation in Testing: Still Relevant in 2026?

In this episode, Richard Bradshaw and Vernon discuss the relevance and application of the six principles of automation in testing in the context of AI advancements. They explore how these principles hold up in 2026, the challenges faced in automation, and the future of testing strategies.

00:00 - Intro

01:47 - Welcome (Richard is not at home 👀)

02:07 - Ramadan, cooking without tasting, and plastic teeth 🦷

04:01 - Today's topic: revisiting the AiT principles ahead of a keynote

04:58 - What is Automation in Testing (AiT)?

06:49 - Principle 1: Supporting Testing over Replicating Testing

07:01 - Vernon's take: testing is a performance, not a click sequence

08:22 - What the industry promised vs what automation actually does

08:49 - The serendipity you lose when a human isn't testing

09:59 - Agentic testing: observing more, but still not replicating humans

10:56 - The danger of anthropomorphising AI output

12:10 - LLMs always give an answer — and that's the problem

13:03 - Principle 2: Testability over Automatability

13:14 - Vernon's take: narrow vs broad — operate, control, observe

14:38 - Making apps automatable for the robots but not the humans

15:37 - The shiniest framework in a broken testing context

16:40 - If it's testable, it's probably automatable — but not vice versa

16:55 - Automation strategy vs testing strategy: when they compete, everyone loses

17:46 - The problem has always been testing, not automation

19:57 - Principle 3: Testing Expertise over Coding Expertise

20:18 - Vernon's take: testing expertise lets you leverage the tools

21:47 - The spoonfed tests problem: great at automating, lost without guidance

22:36 - The "code school" era: everyone told to learn to code

22:51 - Coding agents have changed the maths on this

26:01 - The new nuance: test design and framework knowledge over writing the code

28:44 - Evaluating code is a testing problem — and LLMs can help you do it

30:43 - Are agents as good as a junior developer?

31:42 - Outcome Engineering (O16G) and the race to write the AI principles

32:13 - Simon Wardley: we're in the wild west again

33:22 - Principle 4: Problems over Tools

33:29 - Vernon's take: the hammer and the nail

34:07 - Don't let your problems be shaped by the framework you have

34:36 - New automation opportunities beyond testing: PRs, logs, story review

35:30 - Principle 5: Risk over Coverage

36:12 - Vernon's take: 100% coverage ≠ 100% risk coverage

38:00 - The one test case, one automated test fallacy

39:04 - Where in the system is the risk? Do you even know your layers?

39:49 - Probabilistic vs non-deterministic: refining the language around AI

40:53 - Coverage as intentional vs coverage as a number someone picked once

43:15 - Principle 6: Observability over Understanding

43:24 - Vernon's take: just-in-time understanding vs reading everything upfront

44:12 - What the principle was actually about: making automation results observable

47:00 - Does this principle belong in testing, or has it grown into quality?

49:00 - So... what's missing?

50:00 - The four pillars: Strategy, Creation, Usage, and Education

57:05 - Automation in Quality: the bigger opportunity

01:01:00 - Wrap up + Vern's Lead Dev panel

Links to stuff we mentioned during the pod: