DevOps and Software Testing.

Most of my recent work has been with DevOps teams. While in one sense DevOps is another evolution in software development. It also introduces some new skill requirements and responsibilities into the daily routine of a tester.

These diagrams tend to confuse people, hence the video...

I've created a short video to highlight some of these changes and the opportunities they bring. It's not an exhaustive view of DevOps but it gives a highlight of what you could be working with.

While DevOps isn't a panacea to our software development problems, I have found that empowering teams with the ability to build and use the tools they need, can rapidly improve team morale and productivity.

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What possible use could Gen AI be to me? (Part 1)

There’s a great scene in the Simpsons where the Monorail salesman comes to town and everyone (except Lisa of course) is quickly entranced by Monorail fever… He has an answer for every question and guess what? The Monorail will solve all the problems… somehow. The hype around Generative AI can seem a bit like that, and like Monorail-guy the sales-guy’s assure you Gen AI will solve all your problems - but can be pretty vague on the “how” part of the answer. So I’m going to provide a few short guides into how Generative (& other forms of AI) Artificial Intelligence can help you and your team. I’ll pitch the technical level differently for each one, and we’ll start with something fairly not technical: Custom Chatbots. ChatBots these days have evolved from the crude web sales tools of ten years ago, designed to hoover up leads for the sales team. They can now provide informative answers to questions based on documents or websites. If we take the most famous: Chat GPT 4. If we ignore the

Can Gen-AI understand Payments?

When it comes to rolling out updates to large complex banking systems, things can get messy quickly. Of course, the holy grail is to have each subsystem work well independently and to do some form of Pact or contract testing – reducing the complex and painful integration work. But nonetheless – at some point you are going to need to see if the dog and the pony can do their show together – and its generally better to do that in a way that doesn’t make millions of pounds of transactions fail – in a highly public manner, in production. (This post is based on my recent lightning talk at PyData London ) For the last few years, I’ve worked in the world of high value, real time and cross border payments, And one of the sticking points in bank [software] integration is message generation. A lot of time is spent dreaming up and creating those messages, then maintaining what you have just built. The world of payments runs on messages, these days they are often XML messages – and they can be pa

Is your ChatBot actually using your data?

In 316 AD Emperor Constantine issued a new coin, there's nothing too unique about that in itself. But this coin is significant due to its pagan/roman religious symbols. Why is this odd? Constantine had converted himself, and probably with little consultation - his empire to Christianity, years before. Yet the coin shows the emperor and the (pagan) sun god Sol. Looks Legit! While this seems out of place, to us (1700 years later), it's not entirely surprising. Constantine and his people had followed different, older gods for centuries. The people would have been raised and taught the old pagan stories, and when presented with a new narrative it's not surprising they borrowed from and felt comfortable with both. I've seen much the same behaviour with Large Language Models (LLMs) like ChatGPT. You can provide them with fresh new data, from your own documents, but what's to stop it from listening to its old training instead? You could spend a lot of time collating,

investigating software

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