PNSQC 2026: Inside Tesla: The Birth of the Software-Defined Vehicle and Quality in the Age of Autonomy

 Well now... that was quick!

We are already at the end of the 2-day conference. Quite a whirlwind experience, and it's neat to see that the last keynote was the first of the PNSQChronicles we did this year.

See the teaser PNSQChronicles video with Florian Rohde

Florian is talking about the idea of the software-defined car, a topic that we have come to see with both interest and annoyance, but that effectively came to be a thing with the advent of Tesla. So what is a software-defined vehicle? It's the idea that, instead of having a vehicle that is limited mechanically, the vehicle is effectively created one time, one version, and the owner/user can choose what capabilities the vehicle provides with how much they have paid and what features they have paid for. In other words, everyone gets a fully loaded car, but how much of the fully loaded car you actually access is up to you and your payment level.

There are a bunch of software components and microcontroller elements inside any Tesla, so what you choose to get and how your vehicle communicates with the broader world can be effectively controlled and optimized for a variety of users. One platform, all sorts of potential configurations and capabilities.

I remember talking about this with Florian in the interview we did and suggested that there was potentially a bit of an annoyance in having to buy/rent features of a vehicle I already own. Florian points out that, in his estimation, this is actually a beneficial arrangement. I remember buying back when I was a starving musician back in 1990 a Ford Escort Pony. This was the most bare-bones entry-level vehicle imaginable. I bought it with no AC, no power steering, no power brakes, manual transmission, crank windows; it didn't even come with a radio (I put that in myself shortly thereafter). I bought this vehicle because it was literally all I could afford at the time (we're talking a vehicle that cost ~$7000 brand new). It was a surprisingly durable vehicle. One of the neat things about an all-manual vehicle is that there's so little on it that could break. I put 168,000 miles on this vehicle in about 6 years, and then I gave it to my brother, who put another 100,000 miles on it. My point is, this was an insanely good value for a vehicle, but I was locked into the choices I could have afforded in 1990. I didn't really mind that, but there were indeed times when I wish it could have done/been more. So what if I could buy that same $7,000 car, but later on, as I got further along in my career, I had an easy way to enable things like power steering, power brakes, turn on the AC, etc. How would I have reacted? Probably favorably, as long as I felt it was a reasonable trade-off for the functionality offered. 

In any event, I want to thqank FLorian for describing this approach that they put together. I'm not a Tesla owner, but as we discussed the details of this talk, I moved over into the "adopt as you need" category. How cool would it be to be able to buy a vehicle that I may only need 2 WD for, but later on I might decide, "Hey, I think I'd want to have 4WD on this vehicle." A very expensive and time-consuming process with any other vehicle, but how cool would it be to be able to just "turn it on?" This is a fast overview of the details of Florian's talk, and I have to say I found it interesting and refreshing to hear about an industry I have zero career involvement in. Am I entirely on board with the "pay-as-you-go" approach of an already loaded vehicle? Not sure, but it makes a lot more sense now :).

PNSQC 2026: "Testing Without Boundaries in the AI Era" w/ Tariq King

Yay! A Tariq talk.

For those not familiar with Tariq King, he's a great speaker, a great innovator, and a fun person to hear speak. The last time I saw Tariq on stage was back in 2024, when he was talking about the capability of generative AI and what it could do at the time. HE was discussing some of the Turing test details regarding human faces and artwork that were generated by AI and what we could tell vs. what we couldn't.

It's been a couple of years since then, and we have now seen some of the outcomes from these Generative AI tools, as well as the blowback and retrenchment of people's opinions on these technologies. It's made an interesting conundrum for me. How do I reconcile the work that I do that may require AI to even be viable vs. the creative industries I am in where Generative AI is absolutely seen as a bridge too far and that, by using Generative AI in the creative spaces of art and in music, you are seen as a "clown show." We weren't there two years ago, and now we are having different conversations than we were then.

This opens up an interesting element of the Generative AI puzzle, even beyond what we are doing, and now discussing when we should or should not use it. We can see that on Spotify, music that has been generated by AI tools is being delisted and forced takedowns applied. I know that's not something that Tariq is necessarily going to address here, but it may be interesting to discuss. If anyone is interested in my take on that, maybe that's a series I can do here.

In my workday world, using AI or being considered for employment requiring my use of AI is not a matter of nuance or delicacy. If I can't demonstrate I know how to use it in both semantic ways as well as autonomous agents working for me, I'm not going to be employed. So in some cases, I may not be able to take some stance and say "AI for thee but not for me". More to the point, AI has given me a desire to be more focused on testing it and trying to figure out specifically what AI is doing, so I can be part of making sure that AI doesn't do the wrong stuff.  

I've been having this conversation with a number of people that I am deeply distrustful of any application I can't reasonably test. In short, I'm not "all in" on AI. I had a funny conversation where I compared AI to the early days of MIDI and how people were hugely dismissive and antagonistic towards using MIDI to make music, especially when it came to sequencing in real time in performances. We don't have those conversations any longer because those MIDI pioneers not only showed how challenging it was but also demonstrated through their creative output that they could actually write music and be inventive in their creations. Will Generative AI get to that point in the music space? On one hand, the music that is being created is polished; it's entertaining, and it's well produced. What it isn't is unique or interesting.  It's a sum total of everything that has come before. Same progressions, same melodies, same structures. And of course it would be. It's been trained on the world of music that's out there. So it can recreate all of that. But what it's not good at doing is breaking the paradigms. It's not good at creating something from the heart. Something human. Something organic. Granted, some people may not care a whole lot about any of that. However, for many of us, to quote the late great Chuck Mosley... "We Care a Lot!"

Back to Tariq's direct points, what do we need to be doing to both test these technologies and determine the future we want to make? One of the fascinating things I came to the conclusion of many years ago was: if I found I could live the rest of my life without working, what would I do? I'll not kid anyone; I'd devote myself and my time to Ensign Red, my band (which, I will be fully transparent and honest, has been how I've been spending the majority of my time since being laid off from my last job ;) ). I also said I'd love to get into snowboard R&D, as at the time I last had that thought experiment, I was a competitive snowboarder (retired since 2005, but it was a great ten years :) ). The third thing I'd want to do is advocate for software quality in any way I could, even if I never got paid another penny to do it. That may be a weirdo thing about me, but it is interesting to think about. So far, I have spent a tremendous amount of time still focusing on software quality even when I'm not actively being paid to do it. The fact is, I love testing, and a part of me has always hoped I'd be able to test forever, or until I shuffled off this mortal coil. Well, I have zero control over who will choose to hire me, but I can advocate anyway, and advocate I will. We are dealing with models and tools that make mistakes, and often pretty fundamental mistakes. 

I like Tariq's thoughts that software testers haven't been taken out of the equation, but that our roles and responsibilities are shifting. We are less needed for our actual physical testing acumen. We are more needed for our judgment and clarity of purpose. To borrow from my comments earlier, MIDI itself doesn't write anything that is new or innovative. That's coming from the mind of the musician. I may have tools that can structure chords and chord progressions, but it's still up to me to know my modes, where I can use them, and how to know when they actually sound good. 

I'm reminded of a comment a dear friend of mine taught me back in the early 1990s, at the start of my testing career (and Chuck, if you by chance are reading this, "hi" and "thank you for everything")... "Every tester, every programmer, every person who works in tech needs to know when they can afford their attitude." That is a statement that has stuck with me over the years. Sometimes we are very valuable, and sometimes the things we think are valuable can be devalued overnight. If we cling too tightly to the technical skills we have,  those can be rendered obsolete quickly. The desire to learn, the desire to explore, and the ability to use those skills to pivot to new realities- that's the valuable skill, and that is the honest, real area where we can see if we can really afford our attitude. 

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.