Was there a test for that? No, and there shouldn't be.

The release shipped. For a while, the team felt good. The work was done, the team had achieved something, and that was rewarding.

Unfortunately for the team, it wasn't long before a problem was found. The Product Owner wasn't happy and had asked was going on down there in the galley, do we need new coders? Better ones? Hipster coders?

The release shipped...

After an investigation, some blushes, raised eyebrows and a couple of "Oh... Yeeeah's" they found the cause. A confusion had collided with a bodge, and the result was a mess.

Should they write an automated - test for this problem?

An embarrassing mistake or a misstep can make us feel we have to do something. An action greater than a fix is needed. A penitance needs to be performed, to redeem ourselves, to make us right again.

Sometimes the penitence is best spent adding a test for that issue. Especially if writing that test has a low cost, the frequency of the problem occurring is high or the impact of the problem is substantial.

But often, there is a smarter path. Take for example the opportunity cost. While you add that automated test, What else could your team be doing? For example, you might be able to spend the time fixing that underlying bodge.

As a tester, a highly targeted automated test might give an instant feeling of protection and a helpful dose of CYA. But while you are coding that test - you are not spending as much time:

Looking for root causes, in code and process
Searching for similar assumptions the team made
Thinking laterally about what else might be broken
Talking to your team about how to stop this happening again.

Also, the automated test may just turn out to be more expensive to write than the fix. Sometimes, iterating is the right thing to do - not just regarding isolating the right product but also in honing its quality.

You can learn from failure, it's essential you step back & take a moment to let that happen. Investigating Software can help you do that.

Comments

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

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,

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

investigating software

Search This Blog