Testing vs Chicken

At CEWT #6 we were asking what constitutes good testing. Here's prettied-up versions of the notes I used for my talk.

One line of argument that I've heard in this context and others is that even though it's hard to say what good testing is, we know it when we see it. But there's an obvious potential chicken and egg problem with that.

I'm going to simplify things by assuming that the chicken came first. In my scenario, we'll assert that we can know what good testing would look like for a project which is very similar to projects we've done before, where the results of testing were praised by stakeholders. The problem I'll address is that we don't have anyone who can do that project, so we have to hire. The question is:

What can we do as recruiters, during recruitment, to understand the potential for a candidate to deliver that good testing?

I've been recruiting technical staff for around 15 years, predominantly testers in the last ten, and my approach has changed enormously in that time. Back in the early days, I would largely base my phone screen interviews around a chronological traversal of the candidate's CV asking confirmatory questions. Checking the candidate, if you like.

These days, a CV to me is more of a filter, and a source of potential topics for exploration. I have also spent a lot of time thinking about how I want to advertise positions, and about the kinds of information I want to give and get from each stage of the process, and how I'll attempt to do that.

I have a simple model to help me. I call it the Egg of Testing Recruitment.

The yolk is the core testing stuff; crucial to our identified needs. The white is the other stuff; important to functioning in our context. It supports the yolk.

Some people will tell you that eggs are easy to cook. Some people also think that recruitment is straightforward: identify a need, describe it, find the person who best matches it, hire them, relax. But eggs don't always come out the way the chef intended.

And recruitment likewise. Here'a few reasons why:

multiple important factors
limited time and engagements
a space of reasonable outcomes
a dynamic feedback system

That last one is particularly interesting: as a recruiter, be aware candidates will be looking to align themselves with your needs. If, for example, you do and say things that suggest you favour exploratory testing, then don't be surprised when answers which support their exploratory testing skills start to come.

But recruitment is starting to sound a lot like testing: the extraction of relevant information from an arbitrarily complex system under various constraints. And, if it's testing, I'll want a mission. And if I had a mission I might phrase it something like this:

The kinds of materials you can usually expect in standard hiring rounds are:

a cover letter
a CV
a phone screen
a technical exercise
a face-to-face interview

And then there's others that are reasonably common these days, including:

social media
blog
personal website
open source projects

All of these hold data about the system under test ... erm, about the candidate. I know that some recruiters disregard the cover letter. I love a cover letter. First, it's more data. Second, it is an opportunity for the candidate to speak direct to me, in their own time, in their own words, about the fit of this role to them and them to this role.

When it comes to conversation and exercises, I use the Egg of Testing Recruitment to remind me of what I'm after.

The yolk: when I can interact with the candidate I tend to want to explore core skills that can only really be demonstrated interactively. I'll want to put the candidate in a position where they can't know everything, where there's ambiguity, and see how they deal with it.

Do they ask for clarification, do they tell me what their assumptions are, do they offer caveated answers, do they say "in this context ..", do they use safety language? In this respect I regard interviews as more like an audition -- asking the candidate to perform something like a testing task, and being able to explain their thought processes around it.

The white: I'll be looking for reporting, presentation, consistency and the like in the written material. I'll also be noting stuff that could be ways in to understanding other aspects, particular technical expertise that I can ask about, for example. I can't ask for demonstration of all skills, but I can ask behavioural questions such as "can you tell me about a time when someone doubted the value of testing or when someone asked you to justify your testing?"

In the real world, of course, the egg model looks very different.

The yolk and egg cannot be separated so cleanly. But that's OK. In the interview, I can be testing both at once. For example, on any answer the candidate gives I can be looking for consistency. I can gauge the correctness or reasonableness or depth of a series of answers and use them as checks on the candidate's reliability of answering.

Having explored the candidate using conversation and exercises I need to evaluate them. A job advert that reflects what you actually want helps here. (It's worth remembering that when you're writing it.)

This evaluation is again like testing; you've stopped because you've spent the time that is available. Of course you could have done more. Of course you could have taken alternative routes. But you didn't and now you have to report: what you did, what you found, and the values and risks associated with that.

In your day job this probably goes to a stakeholder who ultimately makes a decision. In recruitment scenarios, you may well also be the stakeholder. But that shouldn't alter the way you go about your business, unless it makes you care even more than you would normally to do a good job.

I think there's three major points here. To put yourself in a position to recruit testers who can do the kind of good testing you're after:

understand your mission
treat interviews as auditions
explore the candidate

Here's my slides:

Meet Me Halfway?

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How do I Test AI?

Recently a few people have asked me how I test AI. I'm happy to share my experiences, but I frame the question more broadly, perhaps something like this: what kinds of things do I consider when testing systems with artificial intelligence components . I freestyled liberally the first time I answered but when the question came up again I thought I'd write a few bullets to help me remember key things. This post is the latest iteration of that list. Caveats: I'm not an expert; what you see below is a reminder of things to pick up on during conversations so it's quite minimal; it's also messy; it's absolutely not a guide or a set of best practices; each point should be applied in context; the categories are very rough; it's certainly not complete. Also note that I work with teams who really know what they're doing on the domain, tech, and medical safety fronts and some of the things listed here are things they'd typically do some or all of. Testing ...

The Best Programmer Dan Knows

I was pairing with my friend Vernon at work last week, on a tool I've been developing. He was smiling broadly as I talked him through what I'd done because we've been here before. The tool facilitates a task that's time-consuming, inefficient, error-prone, tiresome, and important to get right. Vern knows that those kinds of factors trigger me to change or build something, and that's why he was struggling not to laugh out loud. He held himself together and asked a bunch of sensible questions about the need, the desired outcome, and the approach I'd taken. Then he mentioned a talk by Daniel Terhorst-North, called The Best Programmer I Know, and said that much of it paralleled what he sees me doing. It was my turn to laugh then, because I am not a good programmer, and I thought he knew that already. What I do accept, though, is that I am focussed on the value that programs can give, and getting some of that value as early as possible. He sent me a link to the ta...

Notes on Testing Notes

Ben Dowen pinged me and others on Twitter last week , asking for "a nice concise resource to link to for a blog post - about taking good Testing notes." I didn't have one so I thought I'd write a few words on how I'm doing it at the moment for my work at Ada Health, alongside Ben. You may have read previously that I use a script to upload Markdown-based text files to Confluence . Here's the template that I start from: # Date + Title # Mission # Summary WIP! # Notes Then I fill out what I plan to do. The Mission can be as high or low level as I want it to be. Sometimes, if deeper context might be valuable I'll add a Background subsection to it. I don't fill in the Summary section until the end. It's a high-level overview of what I did, what I found, risks identified, value provided, and so on. Between the Mission and Summary I hope that a reader can see what I initially intended and what actually...

Reasonable Doubt

In Your job is to deliver code you have proven to work Simon Willison writes: As software engineers we ... need to deliver code that works — and we need to include proof that it works as well. He is coming at this from the perspective of LLM-assisted coding, but most of what he says applies in general. I think this is a reasonable consise summary of his requirements for developers: Manual happy paths: get the system into an initial state, exercise the code, check that it has the desired effect on the state. Manual edge cases: no advice given, just a note that skill here is a sign of a senior engineer. Automated tests: should demonstrate the change like Manual happy paths but also fail if the change is reverted. He notes that, even though LLM tooling can write automated tests, it's humans who are accountable for the code and it's on us to "include evidence that it works as it should." Coincidentally, just the week before I read his post I told one of my...

On Herding Cats

Last night I was at the Cambridge Tester meetup for a workshop on leadership. It was a two-parter with Drew Pontikis facilitating conversation about workplace scenarios followed by an AMA with a group of experienced managers. I can't come to work this week, my cat died. Drew opened by asking us what our first thoughts would be as managers on seeing that sentence. Naturally, sadness and sympathy, followed by a week ? for a cat ? and I only got a day for my gran! Then practicalities such as maybe there's company policy that covers that , and then the acknowledgement that it's contextual: perhaps this was a long-time emotional support animal . Having established that management decisions are a mixture of emotion, logic, and contingency Drew noted that most of us don't get training in management or leadership then split us into small groups and confronted us with three situations to talk through: Setting personal development goals for others. Dropping a clange...

Great Shot, Kid

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LLEWT 2024

This weekend I was at LLEWT 2024, a peer conference on Anglesey , north Wales, discussing communication. Given the day jobs of the participants, it was no surprise that the experience reports and the conversations that followed them mostly focussed on software development contexts. Notes from my presentation are in Express, Listen, and Field . I made sketchnotes (below) for each presentation and a mindmap (above) to try to summarise the whole. Without much reflection yet, I guess I would pull these common high-level threads from the day: There are multiple reasons that communication fails ... like, duh! ... but having multiple strategies for framing a message can help ... and having multiple tactics for delivering a message can help too. Understanding what you want from an interaction is key ... so setting the context to make that more likely is wise ... which might mean meta-conversation, being transparent, or changing your approach...

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Exploring It!

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