Provenance & Profiling

Is your car German or Japanese? Are your chocolates from Belgium? And your wine, which country might that be from? There's a good chance you know the answer to some of those questions. Our culture places value on provenance. That is, we care where our possessions originate. It's something we tend to notice.

Warning label?

Furthermore, we ascribe, often without our notice, characteristics to things because of their provenance. For example, that's a Japanese radio - its reliable but not cheap, etc. I often do this un-empirically, without measurement or examination. (that's a flaw)

For software testing, our automatic identification of provenance can be both a useful tool and a distraction.

Noticing where or from whom a feature originated can be enlightening. You may learn over time that a particular team or person tends to implement certain things well, and others things not so well.

This has a tendency to help me to find some bugs relatively easily with individual teams. The first time it may have been time-consuming to see an issue. On subsequent releases, annoyingly easy.

That emotion is useful. Its an indication that you have, maybe subconsciously, profiled the team. You can direct your response to that feeling, in constructive ways. For example, searching for a route cause or suggesting the addition of some unit tests.

It can be useful to think and deliberate over how you may have profiled the teams or people. Again, this is valuable 'intelligence,' the profile can help with planning your time and focusing test automation efforts. Though, the pattern also embodies a form of bias. You should be aware that you are subject to this bias, and routinely double check your assumptions.

It would be easy to concentrate too long on easy to find - known unknowns that were easy to spot thanks to your biased view. To increase our opportunities for discovering new types of issues, set aside time for investigating the app in other ways.

Comments

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 ...

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...

Manumation, the worst best practice.

There is a pattern I see with many clients, often enough that I sought out a word to describe it: Manumation, A sort of well-meaning automation that usually requires frequent, extensive and expensive intervention to keep it 'working'. You have probably seen it, the build server that needs a prod and a restart 'when things get a bit busy'. Or a deployment tool that, 'gets confused' and a 'test suite' that just needs another run or three. The cause can be any number of the usual suspects - a corporate standard tool warped 5 ways to make it fit what your team needs. A one-off script 'that manager' decided was an investment and needed to be re-used... A well-intended attempt to 'automate all the things' that achieved the opposite. They result in a manually intensive - automated process, where your team is like a character in the movie Metropolis, fighting with levers all day, just to keep the lights on upstairs. Manual-automation, manu...

investigating software

Search This Blog