investigating software

  A nervous tourist, glances back and forth between their phone and the street sign. They then rotate their phone 180 degrees, pauses, blink and frown. The lost traveller, flags a nearby ‘local’ (the passer-by has a dog on a lead.   “Excuse me…” she squeaks, “How may I get to Tower Hill?” “Well, that’ s a good one” ponders the dog walker, “You know…” “Yes?” queries the tourist hopefully. “Yeah…” A long pause ensues then, “Well I wouldn’t start from here” He states confidently. The tourist almost visibly deflates and starts looking for an exit. That’s often how we start off in software testing. Despite the flood of methodologies, tips on pairing, power of three-ing, backlog grooming, automating, refining and all the other … ings ) We often find ourselves having to figure out and therefore ‘test’ a piece of software by us ing it. And that’s good. Its powerful, and effective if done right. But, like our dog walker, we can sometimes find ourselves somewhere unfamiliar...

 The beginning of the hitch-hikers guide to the galaxy leads with an alien ship about to destroy the Earth, and the aliens saying we (mankind) should have been more prepared – as a notice had been on display quite clearly – on Alpha Centauri the nearby star system, for 50 years. Seriously, people - what are you moaning about – get with the program?  The book then continues with the theme of bureaucratic rigidity and shallow interpretations of limited data. E.g. The titular guide’s description of the entire Earth is one word: “Harmless”, but after extensive review the new edition will state: “Mostly harmless”. Arthur Dent argues with the Vogons about poor data access This rings true for many software testing work, especially those with externally developed software, be that external to the team or external to the company. The same approaches that teams use to develop their locally developed usually don’t work well. This leads to a large suite of shallow tests that are usually h...

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 ...

 As I write this, my dog is grumpily moving around the room pausing intermittently to give me disappointed looks - looks that only my elderly mother could compete with. She (my dog) is annoyed by the robot vacuum cleaner. Its not been run for a while in that room - and its making a noisy foray into dark corners in a valiant effort to cleanse the mess. Its grinding gears and the cloud of dust in its wake is not helping to ease the dogs nerves. The dog's pleading puppy dog eyes & emotions have of course been anthropomorphised - at least a bit - by me (My dog is 7 years old and weighs over 20kg - so has little to fear). That is - I've taken human feelings and mapped them onto my dog. I know she has emotions - but she lacks language - or at least a language that (1) we humans understand, (2) maps to the same phrases or concepts I'm using. But I'm human, That's how I think and how I interact with people and sometimes - machines. Deciphering the problem and representi...

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...

 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 collati...