PNSQC 2026: "Communication Challenges in the Intersection of Diversity, Agility, and Leadership"

I had a lot of fun talking with Dr. Gwen Avington and Diana Larwsen for our PNSQChronicles interview for this talk, so while I can't be in person to experience it, I can at least appreciate a lot of what they are talking about.

PNSQChronicles interview with Dr. Gwen Avington and Diana Larsen

In the talk today, we were presented with an image for a brief period of time and then asked to say what we saw in the picture. As can be guessed, the image was not clear, and there was a lot of interpretation as to what was in the image (as a remote viewer, I had an even fuzzier view of the image, so it was interesting to hear what people in the room saw.

Quality is one of those areas that we talk a mean game about, and we know we should care about it. However, oftentimes our first quality hits (as in the bad kind, not the sensation kind) tend to come about because of poor communication. We don't have a clear understanding of what we should be doing. It doesn't matter who the person in the room is, or what the responsibility is. If we don't have a clear idea of what the problem is that needs to be solved or addressed, we are already at a disadvantage. Communication can absolutely help to mitigate risks and help avoid costs later on because we didn't discuss or understand the issues.

If we have conversations about quality or lack thereof later in the process, we will have to find ways to shoehorn those capabilities or quality measures in at a point when, had we done it earlier, we could have avoided so many issues. This is definitely an issue for newer people. We might feel like we don't have the status or the understanding necessary to speak up in early stages. We often mistake longevity for expertise and knowledge. We may have questions, but we fear being seen as dumb or lacking. Perhaps it behooves us to say, "Hey, I may be new and lacking information, but have we considered 'X' here?" I've had those experiences, and I've learned that it is valuable to have that attitude. Just because someone has experience or longevity doesn't mean they necessarily know if they have considered areas. Even if they have, they may not know that they are not communicating it well.

So how does diversity work into this? Sadly, a lot of the time, there's a set idea of who has expertise and understanding. We make mistakes all the time in this capacity. Everyone brings something of value to the table, and sometimes the best insights may come from people who may not be as obvious. Sexism, racism, classism, and other "isms" can get in the way of who we think may be able to offer insights of value.

We always get down to the question of what we perceive quality to be. One person will have some different ideas compared to another person. We are all used to the idea that "Quality is something of value to some person(s) who matter". That's a part that we often struggle with. We think that we have an idea of what quality is, but we might not be thinking of the areas that are actually important to the people who are most concerned with it. We also need to know when to reinforce and encourage people to show that their input and observations are both important and valued. No one wants to speak up if they think they will be shot down. It takes courage to open up and communicate. Additionally, help foster an environment where they can step up to speak and know that we have the space to speak in the first place.

 

PNSQC 2026: "With Great Judgment Comes Great Great Responsibility" with Paul Grizzaffi

Good morning again. This is one of the reasons I am sad to not be in person at PNSQC. Paul and I go way back, and I always have fun when I hear him speak. We frequently bond over our love of music, especially heavy metal, so Paul, just know that I'd be bending your ear if I were there ;).

How many of us know what a Pyrrhic Victory is? Pyrrhus of Epirus is famous for winning battles with absolutely dreadful losses, so much so that the term "Pyrrhic victory" means a win that comes at such a great cost it's arguable if it was actually worth it.

PNSQChronicles Podcast previewing the details of Paul's talk

AS testers, we are often asked to "do the right thing" (a Spike Lee joint ;) ). For the few of you out there who chuckled, you're my people... I love you :). Automation is something that has a specific purpose. We can argue all day long if that purpose is actually needed or necessary, but for many of us, our jobs would be rendered very difficult without it. We all do some form of automation if we have done something enough times. That's often the first wave of automation, the "Oh my stars! If I have to do this process one more time by hand, I'm going to lose my mind." I'd argue that the people who are least fond of these steps are probably the ones who will embrace and get good at automation the quickest.

In this current age of AI, we have a fresh new danger. AI can write all of the tests and even the test code for us. In truth, it can. However, if we think that we are going to get "one and done" tests that will be resilient, durable, and effective over time, there may be some surprises in your future. In my last gig, I had the experience of doing both roll-your-own automation and working with AI to write the test code and run it. As I was working through those processes, I came to a simple conclusion. The time to have a vetted and confirmed version of automated tests that AI generated vs. tests I created myself... was roughly equal. That's not to say that I'm some amazing automator (I mean, I know some stuff ;) ) and no question, AI can crank out code so much faster than I can. Still, when I question the code and ask why it's doing what it does, the rounds of revisions to get to a truly working and robust version of tests, at the end of the day, were roughly even.

For those who know how I do these, I'm writing this as I'm listening to Paul, but I'm not going to regurgitate his talk. I am going to consider what he is saying and apply it to my own experiences and see what rings true and what I might learn something fresh from. There are absolutely costs associated with using AI, even if it feels like it is cranking stuff out that feels fast, fresh, and amazing. One of the projects I worked on was to take our Documentation as published and make an analysis gap of what tests we had and what options our documentation was saying we did. While I will not claim that's the be-all and end-all of what should be automated, if you want to do an interesting exercise, I recommend doing that process. You can do it all on your own, or you can run it through AI, but either way, it gives you a simple place to start. Do we stop there? Of course not, but at the minimum, you can say that you are making tests that at least cover the areas that you would want to encourage your customers to be doing. Start with the happy paths and then look for the areas that could trip you up. Look for the error code that exists, and then try to create tests that surface all of that error code effectively. Again, whether you do that as a roll-your-own project or you turn over the considerations to Claude Code, OpenAI Codex, or some other AI methodology, ultimately, we want to know what's happening and why.

As a musician, I have many tools that are at my disposal. I use Digital Audio Workstations to do actual recording; I use multiple amp sims and effects that are built into plugins, and I have lots of virtual instruments (even singing voices) so that I can experiment with various things. Some people might remember the early 80s and the advent of MIDI and sequencing software. I remember well the antipathy people had toward MIDI back then, and the feeling that "people were pretending to be musicians" by using MIDI and sequencers. I'd argue that any performer who chose to bring MIDI onstage in those days and orchestrate all of that in a live performance was a braver soul than I. It was easier, in my mind, to just perform than try to wrangle all of that stuff in real time. I remember the snide remarks coming from the metal community especially. The irony is that, a couple of decades later, you would be hard-pressed to hear a metal album produced today that isn't using all of the software recording features that exist today. The beat alignment to the grid and the level of precision that is orchestrated at the point of recording is absurd now. Is it easier than performing it? It might well be. Is there still a performance to be heard there? That's another question entirely. If we are going through and we are massaging every guitar part with quantizing, aligning every beat to the grid, and using Melodyne to clean up every vocal note's tuning, what we end up with may be a pristine recording, an elaborate artifact, but we can't call it a "performance".

To borrow from Jurassic Park, sometimes we get so caught up with what we can do that we might lose sight of what we should do. Knowing when those two options align, and when they don't, is the big thing. I wish I could be there in person to riff with Paul about this, but I hope he enjoys this rambling missive in the meantime :).

PNSQC 2026: The AI Assurance Imperative: Quality Engineering for the Agentic Era w/ Jonathon Wright

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.