Hello everyone, and for those at PNSQC this year, I hope you are having a wonderful time. I, sadly, am not in Portland for the first time in many years, and I am genuinely sad about that. There are reasons I can't be there this year, and I hope that I can participate next time, but for now I am happy and grateful that virtual PNSQC is an option.
Many of you may have seen my PNSQChronicles interviews over the past few months where I've had a chance to talk wth many of the presenters, and Jonathon was a fun interview for me, so I think it's a good bet that you all will enjoy his talk, too.
Jonathon just made a point about the latest series about AI that Netflix has been airing, and pointed out that Sam Altman has said that the most important thing that these companies could be doing is testing.
At the same time, we are dealing with AI agents that operate independently, generate code, run tests, make choices, and develop systems without human involvement. So how do we actually test stuff like this?
Self driving card, next-gen technology, and everything we are looking at today is aiming for stuff that is wildly different than what we are used to. We are talking about new sciences, ways of using technology that we have not even considered, and many of us are still testing things for the paradigms that currently exist. Nothing wrong with that, but the most value we can add is to actually consider how to test for what comes next.
The Human in the Loop is a common phrase, and something we should all make sure that we advocate for. We cannot let these systems run without some level of human judgment making sense of what's coming out of it. We have the phrase "Agentic" now that we need to embrace. It's a fad; it's an experiment. We've all heard that, right? That's likely not going to be true. We have reached a point where so much stuff is coming out now that there is simply no way that we can keep up with it, much less test it effectively.
So while we have Sam Altman saying the most important thing is testing, why does it feel like actual testing is not being included in this conversation, or if it is, it feels very peripheral? We are currently looking at a situation where seasoned engineers are struggling to find work (raises hand: I'm one of those people at present). Don't get me wrong, I haven't given up hope here, but I am certainly curious as to how we are going to address these situations if literally seasoned programmers and engineers are being pushed out at this stage. So if testing is going to be needed in this Agentic AI age, who's going to do it if we are actively removing people? This seems to be an interesting conundrum. Either AI systems are going to have to learn how to effectively test on their own, or people are going to have to help bridge that gap. Again, it's an interesting question: how much are we willing to give of ourselves to help make AI better able to test itself, and perhaps risk losing our place in being able to be part of that conversation in the first place?
The intriguing part of all of this is wrapping our heads around ways that we as people could be effective in this new world. What methods will we use? What approaches are we going to pioneer that we have not yet had the chance to get a feel for? How much of what we are going to need to test do people actually even understand at this point? I don't say that lightly. To borrow from a favorite quote: "The significant problems of today will not be fixed by the same logic and reasoning that created them." (I'll have to go back and give that proper attribution and wording, but that's how I remember it right now). Jonathon is talking about the idea that we can literally load multiple testers' experiences and approaches into a database and effectively have those testers working for him (including me, if they decided my corpus of test writing is worth anything ;) ). That's an intriguing option to think I could have Angie Jones, Randy Rice, or Matt Heusser at my immediate beck and call because I could create agents that would test based on their writing and principles that they have published to apply to my own tasks.
This is a wild new world we are playing in now. I don't have a crystal ball, and I can't say what will be happening next year, much less five years. The key Jonathon is trying to point out is that we don't have five years to absorb this. This is a reality we are going to have to embrace really quickly, and we ignore it at our peril. It's proving to be an expensive option for many, but don't assume that means that it's going to just fall apart. AI is here, enmeshed, and not going away. Testing it is going to be critical to it not running off the rails. I'm glad to see some people are actually considering the testing needs and realizing the elephant in the room. How we go about doing it is anyone's guess.
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