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Ady Stokes

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Ady Stokes
Freelance Consultant
He / Him

MoT Ambassador. Currently semi-retired and freelancing. I am, amongst other things, a writer, speaker, and accessibility advocate. Leeds Chapter Lead. MoT Certs curator and article editor for MoT and WonderProxy. Testing wisdom, friendly, testing songs and poems. Great minds think differently. STEC and SQEC Certified.

🎂 MoTaBirthday | March 11, 2017
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Leeds MoT Chaper Start Speaking Workshop   image
First published in 2020. We had such positive feedback for the session we ran on Thursday the 23rd of January. I thought it might be useful to write up the details as it might help others who ...
5 Aug
A tester walks into a security conference  image
First published March 2021 There’s no punchline. sorry.    This post is all about me attending my very first Security Conference at BSides Leeds on Friday the 24th of January 2020. T...
5 Aug
Staying positive in a pandemic (a personal story)  image
This was first written in 2021. I pondered whether to share this as a moment. It is from my blog The Big Test Theory, which will shortly be no more. In the end, I decided it was a positive story ab...
5 Aug
Slack bug?  image
Anyone else seen a preview upside down on Slack?  This was a DM. 
31 Jul
Everyday accessibility: 7 ways to do a little a11y every day image
Modernise your design strategy with mobile-first and keyboard-centric approaches that create more resilient, accessible, and user-friendly software.
5 Mar
What is software quality? image
Transition from reactive quality assurance to proactive quality engineering by embedding shared responsibility throughout the entire development lifecycle
24 Feb
The good stuff: Problems, podcasts, and progress image
A 'good problems' episode about production-testing drama, MoTaverse growth, new voices, lifelong learning, and how community conversations help people keep going.
24 Jul
These wins in quality: Grins, Gary and going for it - Ep 142 image
A celebratory 'wins' episode full of community milestones, personal breakthroughs, bug catches, career confidence, and the many small moments that keep the MoTaverse moving.
10 Jul
Two Factor Authentication (2FA) Two factor authentication, or 2FA, is a security process where a user provides two distinct forms of identification before gaining access to an account, system, or application. In identity and access management (IAM) architectures, 2FA acts as a security gate within the authentication workflow, enforcing multi-layered defence.Authentication Factors look to combine two distinct pieces of information. A knowledge Factor, which could be passwords, PINs, or secret answers. A possession Factor, such as One Time Passcode (OTP), secondary apps e.g., Google Authenticator, Authy, etc.  An Inherence Factor is something you are. Your biometric data for Face ID, a fingerprint scanner, or even iris scans for secret agents or super villains!  For testing, you can mock 2FA endpoints in non-production environments to avoid dependencies on third parties. In automation, secret keys can work in test environments to generate valid OTP tokens within end-to-end UI tests. You can also implement conditional test configurations or feature flags (e.g., test-only OTPs or environment-level 2FA bypass) specifically for CI/CD pipeline runs. OTP (One Time Passcode) An OTP is an automatically generated and secure numeric or alphanumeric string of characters that authenticates a user for a single transaction or login session.  Unlike the usual run of the mill passwords you use to log in to a site or app, an OTP (One Time Passcode) is valid for one use only. They are sent to previously provided details such as a phone number or email address. They typically expire after a short duration. Something like 1 to 5 minutes is typical, but some can be 10 minutes. It serves as a second layer of defence in Two Factor Authentication (2FA) and Multi Factor Authentication (MFA). They can also come from authentication apps or be physical hardware tokens.  AI Artificial Intelligence Artificial Intelligence is the broad science of making computers mimic human decision-making and problem-solving. It is a nested family of technologies. At the outer edge, you have general AI. Inside that, you find machine learning, where systems learn from patterns in data. At the very centre, you have deep learning. It tries to mimic the human brain using neural networks. AI is a powerful tool, but it is not a replacement for human thinking, nor is it ever likely to be. It is brilliant at finding patterns in mountains of data, giving simple answers to complex questions (context depending, of course), but it lacks common sense and empathy. Our job is to bridge that gap, ensuring that AI is safe and reliable.  Default thinking Default thinking is the natural, preferred approach for a tester when they look at a problem. It is the familiar path the mind takes based on its knowledge and experience. Everyone has their own starting point that feels comfortable. Whatever it is, it is just the first step of thinking. Default thinking is not wrong. It is a habit, shaped by experience, that develops over time. The important part is recognising that this first step is only the beginning of thinking. Testing becomes deeper when you think from different perspectives. Once you pass your starting point, you can choose to explore in other ways. There are many mindsets to learn, and you can strengthen your thinking with practice. The aim is not to remove the natural tendency. It is to expand it. When you understand your default, you can use it as a starting point rather than a place to stay. This gives you the freedom to choose the mindsets that best fit the problem. Feedback density Imagine driving with a GPS that updates every second versus one that recalculates every ten minutes. Both eventually get you to your destination, but one helps you correct mistakes almost immediately. Feedback density is how frequently your team receives useful information that helps it make better decisions.This is what feedback density is. It is the amount of useful learning a team generates over a given period of time. A team with high feedback density learns continuously through questions, reviews, testing, monitoring, and collaboration. A team with low feedback density spends the same amount of time working but learns much later, making mistakes more expensive to fix.Hanisha Arora
05 Aug
bug-story
security
29 Jul
a11y
accessibility
27 Jul
a11y
accessibility
Accessibility myth. Alt-text (www.linkedin.com)
25 Jul
a11y
accessibility
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