Breaking the Cycle: Leading Organizations That Thrive Amid Constant Change with Allan Rennebo Jepsen (a PNSQC Live BLog)

Well, that was fast! We have reached the end of the conference with our final keynote. There has been a lot to absorb and learn over the past few days, and I've enjoyed my time here. With that, here's the closing talk for PNSQC 2025.

Allan Rennebo Jepsen is a leadership expert and author based in Copenhagen, Denmark, who has spent over two decades helping organizations adapt to change rather than fighting it. Allan opened up with an example of an online banking initiative that went horribly wrong, where everything that could go wrong did, to the point where the CEO at a corporate meeting said that a large majority of customers of the bank would not be around a year from now. Meaning their business model was cratering and they risked going out of business. Change is needed, but how in the world are they going to do it? Large banks are optimized for compliance, and because of that, they are literally designed to preserve the status quo. 

PNSQChronicles: Leading Organizations That Thrive Amid Constant Change with Allan Rennebo Jepsen

So what ends up happening? “We are trapped in a loop of meetings, updates, and alignment sessions, with initiatives and plans made, but somehow, progress keeps slipping away.” 

This ongoing “cycle of coordination” eats up energy and time while creating an illusion of progress. Wouldn't it be nice to be able to break the cycle and replace it with focused, adaptive, and empowered teams with goals and efforts that can actually move the organization forward?

As Allen points out, the one true constant is change, and change is not slowing down. New technologies, shifting customer expectations, and global competition mean that businesses must evolve faster than ever before. “Success today requires a new paradigm, one that values bold leadership over small tweaks.”

Many organizations instead fall into reactive patterns and mindsets. They organize based on what the business thinks is important, rather than looking at what the actual customers need. Every new change triggers a cascade of alignment meetings, but before that alignment solidifies, another change hits. The result is paralysis through coordination.

What will it take to break out of this vicious cycle? Genuine adaptability, that's what. Organizations that show that they can learn, adjust, and act decisively in uncertain conditions are the ones that will ultimately thrive.

What do we need to do to actually make change happen?

Strategic Prioritization

We need to focus our energy where it matters most. Not every initiative deserves attention. Leaders need to be ruthless in prioritizing efforts that drive real impact and cut through the noise of constant change.

Winning Together

Too many departments chase local wins at the expense of the organization’s overall success. Instead, focus on organizational collaboration and allow for genuine long-term thinking. Optimizing one team while degrading the system as a whole is counterproductive.

Thriving Amid Change

When teams are empowered to make decisions and take action without waiting for endless approvals, they can develop true adaptability. With those experiences, the team can get better and address challenges related to change. Success likelihood grows when teams have both the authority and the accountability to act quickly. Resilience and adaptability are learned competencies, not traits reserved for a few innovative companies. If you want to be resilient and adaptive, you need to practice and work towards being resilient and adaptive.

Allan encourages creating a “low-risk, high-impact” framework for building organizations that are genuinely adaptable. Continuous learning loops, clear ownership, and a culture of shared goals help with and reinforce the opportunities to effectively adapt to change.

As Allen points out, “These organizations didn’t get there by accident. They created the conditions where adaptability could flourish.”

Artificial intelligence, decentralized decision-making, and global competition are reshaping how organizations operate. Instead of fearing disruption, leaders should view it as a constant condition to master. Focus on adaptability as the ultimate organizational competency.

“You can’t predict the next disruption, but you can prepare your organization to handle whatever comes next.”

Key Takeaways

- Prioritize what truly matters. Avoid being spread thin across too many initiatives.

- Empower your teams. Give them the clarity and authority to act.

- Collaborate across silos. Success comes from shared goals, not isolated wins.

- Embrace continuous learning. Every challenge is a chance to adapt and improve.

If there's any one message or idea to bring home today, it is that change doesn’t have to be chaotic. Leaders who build adaptable, empowered teams will be the ones who turn constant change into a competitive advantage.

Want to know more? Allan’s book “Impactful Organizations – And How to Become One!" expands on many of the ideas shared today. 

RAG to the Rescue: Reimagining Enterprise Unit Test Management with AI (a PNSQC live blog)

There is an entire paper with a lot of great information behind this session, so rather than try to recap all of that, I actively encourage all to Read the paper (PDF). Additionally, this paper is a joint effort with Gaurav Rathor, Ajay Chandrakant Bhosle, and Nikhil Y Joshi. With that out of the way...

RAG is a practical framework that blends Retrieval Augmented Generation into the Model Context Protocol to help generate and manage unit tests in large enterprises. Test creation, governance, auditability, and measurable business impact are key components considered by this approach. Again, there's a lot to dissect here, so check out the paper specifically for details.

Unit testing in big organizations is challenging, no question. Legacy code, multiple frameworks, technical debt, uncertain code coverage, and long fix times are all issues that may of us are familiar with. The authors suggest using AI to assist with test generation as well las test management.

Neat. So how would that work?

The proposal consists of four major parts that work together as a microservice.

• AI Agents interpret intent, plan steps, use memory, and invoke tools. They orchestrate the workflow rather than directly hitting data stores.
• Model Context Protocol acts as a secure, typed gateway between agents and external systems. Think of it as a translator and auditor that controls what context reaches the model.
• RAG Knowledge Layer retrieves code, prior tests, specs, domain docs, and compliance rules from a vector database to ground prompts in the organization’s reality.
• LLM Generator builds unit tests from that curated context, returning deployable test classes to clients.

Given a request to create tests for a class, the agent pulls the file and metadata, queries the vector database by package and class, and retrieves related classes, utility code, mocks, example tests, and notes. That bundle becomes a structured prompt, delivered to the LLM through MCP. The output is a test class that aligns with domain logic and existing patterns.

The pilot for this  system was aimed towards a specific and critical micocroservice. The team accomplished automated unit test generation, which created many quick coverage wins, and also helped track time saved and defect trends discovered.

According to Gaurav, ROI modeling compared manual test creation to be about four hours per case. With this new RAG driven approach, they were able to cut this time down to about one hour, including review. 

Again, there is a lot to take in from these examples and I encourage checking out the paper (linked above) for the specifics.

The Ethics of Quality with Gwen Iarussi (a PNSQC Live Blog)

I'm back. I spent the morning moderating the Management, Leadership & People track, so I wasn't able to do that and live blog like I normaly do but let's suffice it to say that Sophia McKeever (Quality With Hearts Aligned - Bolstering Your Quality with Emotional Intelligence), Heather Wilcox (Is the 'Iron Triangle' Dead?) and Kristine O'Connor (Quality Intelligence: Embedding Customer Voice to Drive Agile Excellence) all did great jobs and if you get a chance to see their talks, do so :).

For the first session this afternoon, I get to see long time friend (and actual hirer of my consulting services during my stint in the wilderness) Gwen Iarussi discuss The Ethics of Quality

PNSQChronicles: The Ethics of Quality & Quality as a Living System with Gwen Iarussi

A great quote to start out this conversation is, "Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should." (Dr. Ian Malcolm, Jurassic Park).  You may well remember this line and it is just as valid a warning about unchecked innovation. It's not enough to ensure a product "works". Additionally, we have a continued need to advocate for not just that something works, but that it works appropriately. We don't have the luxury of testing blindly and ignoring the possible impacts of our work and efforts. 

I remember back in the days of my Cisco Systems work life that the NetFlow protocol tracking software was developed. I was on the team that was ultimately codifying the "call record" or any and all internet data. I remember part of me was a little leery about what we were doing but I recall being told by one of the developers that, "if we don't develop this with our approach and methodology and ethics, then our competition will create it, and we will have no control over the ethics that they choose (or do not choose) to use."We can argue whether or not that was a good idea (we do of course have end to end tracking of all network data but we also have the ability to encrypt that data as well, so yin/yang, I guess ;) ).

My point with that last example may be seen as trivial today but part of me genuinely wondered if our efforts were being done in the appropriate place. At that time, I was young and unsure of my voice, so I just rolled with it. Today me would be less likely to do that, I'd want to ask a number of questions. Granted, I may not have been able to have modified or altered any of the outcomes, but I certainly would have been more direct and focused with my interactions and questions. I would have advocated for positions I felt we should represent.

Our tech environment today is getting even more fragmented and over the top. Speed of execution is taking precedence over the quality and ethics of what we are creating. We have all sorts of areas where the haves are taking advantage of the have nots through these systems, and it's very much by design. Let's not forget the fact that this age of AI and fast, ubiquitous tech is consuming land, fuel and water resources at an alarming rate. Much of the tech advances and buildout to host and serve it is not sustainable in the long term. At least not if we want to make sure that people have access to water, fuel, and food a few years from now.

Much of this comes down to accountability. How do we develop accountability and stand for what we want to see happen in our environments? How do we make our ethics more than just platitudes? By us building a culture of accountability, we are able to foster a culture of ethics and making considerations that go beyond merely speed and efficiency.

Ultimately, this all comes down to a quote that is attributed to Sitting Bull, "Let us put our minds together and see what life we can make for our children". Ultimately, every good or bad decision we make may have an effect on us, but they will surely and certainly effect future generations. With this isn mind, I would absolutely say it would be in our best interests, including business interests, to be able to leave to our children a world and systems that have effective safeguards to prevent our actively destroying our environment, whatever and wherever those may be.

Monsters & Magicians: Testing the Illusions of Generative AI with Ben Simo (a PNSQC Live Blog)

Day 2 of the main program is underway (Day 3 includes the workshop presentations). It's crazy to see/feel how quickly this event goes by. As always, I've had a great time at this event and enjoyed my interactions with everyone. It's especially neat to realize how many people I know in this space, and when keynote speakers are literal friends, such as today with Ben Simo. 

Machines to do things beyond our physical or mental powers have existed for thousands of years. We can go back to the Antikytheria Mechanism for what may quite possibly be the world's first "artificial intelligence," depending on how you want to interpret that term. Over time, as we have come to grips with and developed an understanding of the rules, laws, and repeatability of activities, what was magical once upon a time has become commonplace in our everyday use. 

PNSQChronicles: Brief Interview of "Monsters and Magicians" with Ben Simo on YouTube

We now see Large Language Models and predictive text generation as the current amazeballs part of our reality. Many people are excited about these technologies, but at the same time, there are many risks surfacing, with reports of organizations suffering actual harm or damage because of using AI tools. We have heard of apps that have jacked up rates arbitrarily, published legal documents with no basis in law, fact, or reality, and taken models of "virtual people" who learn from interactions and the biases and inputs trained these models to be incredibly racist and hateful. 

These situations point to an interesting set of questions: how specifically can we as testers benefit from this wild new world of seemingly random query and response systems? How do we test software that produces inexplicable fuzzy outputs? At the end of the day, software deals with patterns and algorithms. We have technologies such as machine learning, clustering, and ways that data can be grouped and sorted. If we give LLM's a closed data set of information and ask it to work with just that information, it does a remarkably good job of transforming or "creating" work and assets. The key here is that we have given it a known and bounded set of information. Because it is bounded, it is working with a known set of information and can be guided specifically as to what to do with it. As we open it to the outer world and give it fewer controls or restrictions, we open the model up to having to look at vague clusters of data with potentially dubious provenience. A great example of this that I saw in practice during one of the workshops was with the idea of creating spec documents in markdown that would reside at the base of your document tree. By making the spec document the oracle of choice, and giving the instructions that the spec document was the arbiter of what the model was to do, we limit the chance of hallucinations and odd reactions considerably. Not completely, but we make it much easier to track what the model is doing.

An example I had fun with recently was when I heard through a podcast a story of the son of Hephaistos who went to Olympus during the waning days of the Greek pantheon's influence and took a remnant of the fire from Olympus (in an homage to the myth of Prometheus). However, something about this seemed "off", as in it was being presented as an ancient myth, but it clearly wasn't. Could we identify where the original story came from? Through various prompts, reviewing the text transcript of the story, and other clues, the LLM determined that, indeed, it was a modern story being told in the manner of an ancient Greek myth, and even noted that the delivery of the story in meter and timing mimicked closely the delivery of Hesiod. I was not able to determine who wrote the story or where to find it on the internet, but the details it did provide were interesting. Many of them felt fanciful, but all of them felt plausible. That's the danger with LLM output. Unless we are diligent, the very plausibility of the output could be accepted easily as though it were fact. Closer inspection found numerous areas that were not accurate (referencing older myths that I was aware of and had history with, but attributing individuals and characters that didn't belong there). People who may not have this knowledge or familiarity might accept what's being presented as fact because it flows so naturally and just "feels right and authoritative". 

In these cases, the Boolean PASS vs FAIL rationale doesn't work. It's not that the tests pass or fail, but that large elements do pass, but there are outputs that are "off". It's not a total fail, but it's also "corrupted" in a way that we cannot simply rely on the output. Additionally, we can run tests multiple times and get slightly different outputs with the exact same information being presented. In my own world of testing AI, we use a variety of benchmarks and monitors that help us determine if the models are behaving in ways that we expect them to. We have a variety of tests and models that allow us to determine things like Performance drift, comprehensive analysis, bias drift and disparity, the currency of the data, and comparing for homoscedasticity (a $10 word that means looking at the variance of the errors in a model and determining if it is constant/consistent across all of our observations). 

A neat tool that we have is the "AI Risk Repository" which helps us identify risks and domains of use of models where the various risks can be found. By looking at the areas that are potential risks, we can better be informed or consider aspects we can apply to our testing efforts.

Ultimately, one of the key takeaways from this talk is the idea that we are at peril of being beguiled by the magic surrounding us. We want to be responsible with our use of AI, and thus, we need to test and consider how best to apply what we learn and spread that knowledge amongst our colleagues. Magic is often sleight of hand, and it's important that we understand and can understand how that sleight of hand is performed.

Dance Bot Showcase at PNSQC: Where Robots Move to the Beat of Quality (a PNSQC Live Blog)

 This is an interesting and unusual activity... unless you have been coming to PNSQC for at least the past 15 years. The various LEGO League entries for talks and demonstrations are nothing new. A robot dance competition? Okay, that's different ;).

This year, PNSQC is hosting the first-ever Dance Bot Showcase—a celebration of creativity, code, and community!

We are starting out with a Q&A with the various team captains and learning about the challenges they faced putting on this event, and determining the criteria necessary to make this event happen.

PNSQC 2025 Robot Dance Off

Teams featured are Gear In Motion, The Beaver Bots, and Mechanical Mages (placing 3rd, 2nd, and 1st, respectively).

The various robotics teams described a number of challenges and issues they faced while preparing for this event or for other events that they have participated in. This particular demonstration is a literal "robot dance off", meaning they need to program and choreograph robots to actually dance in sync with music. These teams demonstrate technical skill as well as showcasing creativity and artistic flair. It’s more than just a performance—it's a hands-on demonstration of how software quality, testing, and intelligent systems intersect in the real world.

Each of the youth teams engages with key software concepts like adaptability, precision, and resilience through robotics. With a mix of live performances, a student-led fireside chat on software and testing challenges related to robot programming and choreography, the Showcase connects generations through shared curiosity and learning. Oh, but that's not all. Judges will develop end-user quality criteria to determine how to evaluate and judge the participants and their robot entries. Just as we would evaluate web application software quality from the end-user perspective, we will see a variety of aspects within the dance bot competition to highlight various quality characteristics.

In addition to the three local teams, we have three international teams (from the Dominican Republic, the Democratic Republic of Congo, and Mauritius) that have also contributed presentations of their dancing robots (also included in the video footage).

Have I piqued your interest? I hope so, because as soon as I can get the footage up, we will showcase the dancing robots on our YouTube channel as well as here.

Web for All: Enhancing Quality Intelligence with Automated Accessibility Testing using Playwright and axe-core with Rodrigo Silva Ferreria

Sometimes what we expect to happen doesn't. I was looking forward to Rodrigo Silva Ferreria speaking about Accessibility testing and Accessibility Automation. However, due to technical difficulties and him not being able to connect to the Zoom session (he was presenting remotely), I was asked to pinch hit at the last minute, which I did (I shared my most recent version of "Senses Working Overtime" that I presented at CAST back in August). However, I was all prepared to do a session on Rodrigo's talk, and I read his paper in anticipation for it, so rather than talk about the presentation I gave, I'm going to talk about the presentation Rodrigo would have given, using his paper as the basis.

One of the recurring themes at PNSQC is that quality is holistic. It’s not just about performance or reliability, but about inclusion and usability for everyone. Rodrigo Silva Ferreira’s paper “Web for All: Enhancing Quality Intelligence with Automated Accessibility Testing using Playwright and axe-core” emphasizes this by showing how teams can bake accessibility (a11y) directly into their test automation workflows.

Rodrigo starts by reminding us that accessibility isn’t optional, it’s a moral, legal, and usability imperative. Yet too many teams still treat it as an afterthought. Manual audits take time, training is scarce, and accessibility issues often get discovered long after release. By then, the fixes are costly, the users are frustrated, and sometimes lawsuitas are incoming.

Manual a11y testing will always have a role, but it doesn’t scale. Rodrigo’s approach uses Playwright in combination with axe-core from Deque Systems. Together, they create an efficient and repeatable testing loop that fits naturally into CI/CD pipelines.

The result: accessibility checks happen early and continuously, not just during audits.

The Tools: Playwright + axe-core

Playwright handles the browser automation (navigating pages, interacting with UI elements, capturing screenshots, etc.) while axe-core brings the accessibility intelligence, scanning for WCAG violations, tagging them by severity, and suggesting fixes.

By taking the @axe-core/playwright package, we can inject axe-core into the current browser context. After a page or component is loaded, it performs an automated scan and outputs results as machine-readable data (JSON/HTML). The flow is go to the page in question, inject the call for axe-core, save the output as an object, and then that will alow you to print out the results as either an HTML or JSON output. What you do with the object contents is entirely up to you.

Setting It Up

The paper walks through the steps of integrating these tools into a test suite:

• Install @axe-core/playwright alongside Playwright
• Configure the environment (Node.js, CI/CD, etc.)
• Add a simple accessibility test — e.g., scan a page and report any violations
• Include tests in GitHub Actions or another CI system so they run automatically on each PR or nightly build

Rodrigo emphasizes keeping it low-maintenance. The goal is to empower QA and dev teams to start small and expand coverage organically.

After implementing the approach, Rodrigo’s team observed:

• Broader coverage of A11Y across key pages and user flows
• Earlier defect detection (shift-left in action)
• Improved team awareness and ownership of accessibility
• Fewer regressions and less rework later in the SDLC

Automation didn’t replace manual audits, it amplified them by catching the easy stuff early, so specialists could focus on deeper, context-specific checks.

Key Takeaways

By integrating A11Y automation into standard test pipelines, teams reinforce the idea that accessibility isn’t “someone else’s problem.” It becomes part of the fabric of software quality.

Rodrigo closes with a challenge: start small. Pick one key flow, one component, one page, and add automated accessibility checks. From there, grow. Over time, this builds a sustainable, inclusive testing strategy that benefits every user.

If you want to try out what Rodrigo suggests, here's what you need to get  started:

• Playwright – Microsoft’s end-to-end testing framework
• axe-core – Deque Systems’ open-source accessibility engine
• WCAG Guidelines – W3C Web Content Accessibility Guidelines

Again, happy to talk to these topics but I had wanted to hear Rodrigo's presentation directly. Hopefully for some, this will be at least a substitute for that.

Being a Change Agent with April K Mills (Welcome to PNSQC Live Blog Stream)

Hello all, and welcome to PNSQC, coming at you from Portland State University Place Hotel and Conference Center. Some of you may be hearing concerning things coming from Portland and having been here for two days now in real time, it is absolutely quiet and normal here (though I must confess that the media I have seen of protests with people in frog and chicken suits, I love how absolutely unserious Portland is. We literally started the conference with "The Beat Goes On" marching band (I will upload and link media when I get the chance).

Everyone is a Change Agent

Our first talk/keynote today is coming from April K. Mills, and her talk is "Everyone is a Change Agent". I had a chance to discuss this wth April in our PNSQChronicles vidcast (will link here later), but the key point here is that change agents don't have to be high-power, in-charge people. Every one of us has the capability, and I'd argue the responsibility, to be the change agents our organizations need. To borrow from an idea I agree with a great deal, "everyone needs to stand for something, even if doing so means we have to stand alone".

One of the ideas that April is recommending we start right now to do and be a change agent is to stand against and fight the spread of "AI Slop." How many of us have found ourselves literally struggling with and drowning in AI-generated content in our organizations that seems to say a bunch but doesn't really mean anything? I have run into this a fair bit in social media, but it is absolutely creeping into publications and corporate marketing missives. I saw a person online coin a phrase I have now started using: "AI;DR".

Note: I use this when I am seeing obvious AI output that has been generated, reads as completely soulless, and you can see that much of it is not factual or is genuinely wrong in various areas. In these cases, I do believe that announcing "AI;DR" is an excellent response.

YouTube: PNSQChronicles episode with April K. Mills

One of the things I have always appreciated (and I'm quoting Matt Heusser is the wording here) when I come to conferences like PNSQC is that I am not looking to upend the entire world with everything I learned. First of all, it's not practical, and second, systems are developed over time, and there may well be entrenched interests to overcome. Thus, it's not practical to come back from a conference with everything under the sun to overturn everything we do. Instead, we need to find a few areas that we can implement and do so without needing permission to do those things. All of us can do that. We find a way to add a new approach, process, or method based on what we learned, and then, in a few weeks or months, when people are curious as to how we have made key improvements, we can then say/demonstrate what we have been doing. There may be varying barriers to exactly how much we can do in certain areas, but we can all do something in a different way. 

When we can implement an under-the-radar change, it's often a good idea to try it out quietly and see if the change is worth pursuing. Often, we can determine pretty quickly if our brilliant idea may not be so brilliant (or at least we discover the key contexts that help explain why we haven't tried doing this before). Make no mistake, that in and of itself is often a valuable exercise. 

Sometimes the best question we can ask is "Why?" There is an idea called the "Power Paradox" that comes from the idea that people can only do so much because they don't have the power to make changes. We hear that in politics and senior management all the time. If only we could get the people at the top to agree with us and make the changes necessary. Truth be told, we don't always need to do that, and sometimes, the best way to break down that false assumption of power is to ask why we do something (strategically, don't become the kid in the back seat asking, "Are we there yet?" every five minutes). By asking "Why?" at strategic times, we may not sway the people at the top, but we may get more of us at lower levels to also ask why, and if more of us start asking why, that often triggers those higher up to realize that this is an issue that is not serving the people it intends to. Sometimes it takes more than just asking why, but strategic action often starts from just that question.

Key Takeaway: Being a change agent doesn't have to be a major initiative, and we don't have to reinvent the wheel to be a change agent. We don't need to have the right boss, with the right budget, with the right revenue, at the right time to make changes. Often, we can make changes just by trying to do some different things, and often, we can do them without having to ask permission to do so. Start there first and work your way up :).

Building Your Tester Survival Guide with Dawn Haynes: A CAST Live Blog

For the past couple of days as we have been getting CAST ready to go, I've gone and done a number of item runs, food stops, and logistical troubleshootings with Dawn Haynes, which is a common occurrence over my years with CAST. Dawn and I have frequently been elbows deep in dealing with the realities of these conferences. One funny thing that we quipped about was the fact that any time we appear at conferences together as speakers, somehow we are always scheduled at the same time (or at least a lot of the time). I thought that was going to be the case this time as well but NO, the schedule has allowed us to not overlap... for ONCE :)!!!

I first learned about Dawn through her training initiatives long before I was actually a conference attendee or speaker. She appeared as a training course provider in "Software Test and Performance" magazine back in the mid 2000s. Point being, Dawn has been an expert in our field for quite some time, and thus ,if Dawn is presenting on a topic, it's a pretty good bet it's worth your time to sit and listen. Daw is the CEO and resident Testing Yogini at PerfTestPlus, so if you want to get a first hand experience with her, I suggest doing it if you can. For now, you get me... try to contain your excitement ;).

Onekey area tht Dawn and I are both aligned on and wholeheartedly agree with is that we are individually as testers, quality professionals, whatever we call ourselves, we are responsible for crating our own careers and if you have been in testing for an extended period, you have probably already had to reinvent yourself at least once or twice. Dawn wants to encourage all testers and quality professionals to actively develop their survival instincts. Does that sound dire. It should... and it shouldn't. Dawn's point is that testing is a flexible field and what is required one day may be old hat and not needed the next. As testers, we are often required to take on different roles and aspects. During my career, I have actually transitioned a few times into doing technical support over active day to day testing.  That's a key part of my active career curation. I've actually been hired as a tech support engineer only for them to realize that I have had a long career in software testing and the next thing I know, I'm back and actively doing software testing full time. In some cases, I have done both simultaneously and that has kept me very busy. My point is, those are examples of ways that testing skills can be applied in many different ways and with many different jobs. 

Automating stuff, doing DevOps, running performance or security audits, or looking at areas your organization may not be actively working towards and playing around with those areas. As you learn more and bring more to the table, don't be surprised that you may be asked to do more of it or leverage those skills to learn about other areas.

Some areas are just not going to be a lot of fun all of the time. Sometimes you will take a while to get the skills you need. You may or may not get the time to do and learn these things but even if you can just spend 20 minutes a day, those efforts add up. Yes, you will be slow, unsure, and wary at first. You may completely suck at the thing that you want to/need to learn. You may have deficiencies in the areas that you need to skill up on. The good news is tat's normal. Everyone goes through this. Even seasoned developers don't know every language or every aspect of the languages they work with. If you are not learning regularly, you will lose ground. I like Dawn's suggestion of a 33/33/33 aproach. Learn something for work, reach out to people, train and take care of yourself. By leveraging these three areas, we can be effective over time and have the healeth and stamina to actually leverage what we are learning. We run the risk of burning ourselves out if we put too much emphasis on one area, so take the time to balance those areas and also, allow yourself to absorb your learning. It may take significant time to get good at something but if you allow yourself the time (not to excess) to absorb what you are learning, odds are you will be better positioned to maintain and even grow those skills.

One of the best skills to develop is to be collaborative whenever possible. Being a tester is great but being able to help get the work done in whatever capacity we can is usually appreciated. A favorite phrase on my end is, "There seems to be a problem here... how can I help?" Honestly, I've never to date been turned down when I've aproached my teams with that attitude.

Glad to have the chance to hear Dawn for a change. Well done. I'm next :).   

We're Back: CAST is in Session: Opening Keynote on Responsible AI (Return of the Live Blog)

 Hello everyone. It has been quitre a while since I've been here (this feels like boilerplate at this point but yes, it feels like conferences and conference sessions are what get me to post most of the time now, so here I am :) ).

I'm at CAST. It has been many years since I've been here. Lots of reasons for that but suffice it to say I ws asl=ked to participate, I accepted, and now I am at the Zion's Bankcorp Tech Center in Midvale, UT (a suburb/neighborhood of Salt Lake City). I'm doing a few things this go around:

- I'm giving a talk about Accessibility and Inclusive Design (Monday, Aug. 25, 2025)

- I'm participating in a book signing for "Software Testing Strategies" (Monday, Aug. 25, 2025)

- I'm delivering a workshop on Accessibility and Inclusive Design (Wednesday, Aug. 27, 2025)

In addition to all of that, I'm donning a Red Shirt and acting as a facilitator/moderator for several sessions, so my standard Live Blog/post every session will by necessity be fewer this go around as I physically will not be able to do that this go around. Nevertheless, I shall do the best I can.

The opening keynote is being delivrered by Olivia Gambelin and she is speaking on "Elevating the Human in the Equation: Responsible Quality Testing in the Age of AI"

Olivia describers herself as an "AI Ethiscist" and she is the author of "Responsible AI". This of course brings us back to a large set of questions and quandaries. For a number of people, we may think of AI in the scope of LLM's like ChatGPT or Claude and many people may be thinking, "What's the big deal? It's just like Google only the next step." While that may be a common sentiment, that's not the full story. AI is creating a much larger load on our power infrastructure. Huge datacenters are being built out that are making tremendous demands on power, water consumption, and on polluion/emissions. It's argued that the growth of AI will effectively consume more of our power grid resources than if we were to entirely convert everyone over to electric vehicles. Thus, we have questions that we need to ask that go beyond just the fact that we are interacting with data and digital representations of information. 

The common refrain of "just because we can do something doesn't necessarily mean that we should". While that is a wonderful sentiment, we have to accept the fact that that ship has sailed. AI is here, it is present, in both trivial and non trivial uses, and all of the footprint issues that that entails. All of us will have to wrestle with what AI means to us, how we use it, and how we might be able to use it responsibly. Note, I am thus far talking about a specific aspect of environmental degradation. I'm not even getting into the ethical concerns when it comes to how we actually look at and represent data. 

AI is often treated as a silver bullet and something that can help us get answers for areas and situations we've perhaps mnot previously considered. One of the bigger questions/challenges is how we get to that information, and who/what is influencing it. AI can be biased based on the data sets that it is provided. Give it a limited amount of data, it will give a limited set of results based on the information it has or how that information was introduced/presented. AI as it exists today is not really "Intelligent". It is excellent pattern recognition and potential predictive text presentation. It's also good at repurposing things that it already knows about. Do you want to keep a newsletter fresh with information you present regularly? AI can do that all day long. We can argue the value add of such an endeavor but I can appreciate for those who have to pump out lots of data on a regukar basis, this is absolutely a game changer.

There are of course a number of areas that are significantly more sophisticated and data that is much more pressing. Medical imaging and interpreting the details provided is something that machines can crunch in a way that a group of humans will take a lot of time to do with their eyes and ears. Still, lots of issues can still come to bear because of these systems. For those not familiar with the "Texas Sharpshooter Fallacy", it's basically the idea of someone shooting a lot of shots into the side of a barn over time. If we draw a circle around the largest cluster of bullets, we can infer that whoever shot those shots was a good marksman. True? Maybe not. We don't know how long it took to shoot those bullets, how many shots are outside of the circle, the ratio of bullets inside vs. outside of the circle, etc. In other words, we could be making assumptions based on how we are grouping something that a bias and prejudice is leaning on. Having people look at these can help us counter those biases but it can also introduce new ones based on the people that have been asked to review the data. To borrow an old quote that I am paraphrasing because I don't remember who said it originally, "We do not see the world for what it is, we see it for who we are".  AI doesn't counteract that tendency, it amplifies it, especially if we are spcifically looking for answers that we want to see. 

Olivia is arguing, convincingly, that AI has great potential but also has significant liabilities. It is an exciting aspect of technology but it is also difficult to pin down as to what it actually provides. Additionally, based on its pattern matching capabilities, AI can be wrong... a lot... but as a friend of mine os fon of saying, "The danger of AI is not that it is often wrong, it's that it is so confidently wrong". It can lull one into a false sense of authority or reality of a situation. Things can seem very plausible and sensible based on our own experiences but the data we are getting can be based on thin air and hallucinations. If those hallucinations scratch a particular itch of ours, we are more inclined to accept the findings/predictions that match our world view. More to the point, we can put our finger on the scale, whether we mean to or not, to influence the answers we get. Responsible AI would make efforts to help combat these tendencies, to help us not just get thr answers that we want to have but help us challenge and refute the answers we are receiving.

From a quality perpective, we need to have a direct conversation as to what/why we would be using AI in the first place. Is AI a decent answer to looking at writing code in ways we might not be 100% familiar? Sure. It can introduce aspects of code that we might not be super familiar with. That's a plus and it's a danger. I can question and check for quality of noutput for areas I know about or have solid familiarity. I am less likely to question areas I am lacking knowledge in or actually look to disprove or challenge the findings. 

For further thoughts and diving deeper on these ideas, I plan to check out "Responsible AI: Implement an Ethical Approach in Your Organization" (Kogan Page Publishing). Maybe y'all should too :).

What Are We Thinking — in the Age of AI? with Michael Bolton (a PNSQC Live Blog)

In November 2022, the release of ChatGPT 3 brought almost overnight the world of the Large Language Model (LLM) to prominence. With its uncanny ability to generate human-like text, it quickly led to lofty promises and predictions. The capabilities of AI seemed limitless—at least according to the hype.

In May 2024, GPT-4o further fueled excitement and skepticism. Some hailed it as the next leap toward an AI-driven utopia. Others, particularly those in the research and software development communities, took a more skeptical approach. The gap between magical claims and the real-world limitations of AI was becoming clearer. 

In his keynote, "What Are We Thinking — in the Age of AI?", Michael Bolton challenges us to reflect on the role of AI in our work, our businesses, and society at large. He invites us to critically assess not just the technology itself, but the hype surrounding it and the beliefs we hold about it.

From the moment ChatGPT 3 debuted, AI has seen a lot of immense fascination and speculation. On one hand, we’ve heard the promises of AI revolutionizing software development, streamlining workflows, and automating complex processes. On the other hand, there have been dire warnings about AI posing an existential threat to jobs, particularly in fields like software testing and development.

For those in the testing community, we may feel weirdly called out. AI tools that can generate code, write test cases, or even perform automated testing tasks raise a fundamental question: Will AI replace testers?

Michael’s being nuanced here. While AI is powerful, it is not infallible. Instead of replacing testers, AI presents an opportunity for testers to elevate their roles. AI may assist in certain tasks, but it cannot replace the critical thinking, problem-solving, and creativity that human testers bring to the table.

One of the most compelling points Bolton makes is that **testing isn’t just about tools and automation**—it’s about **mindset**. Those who fall prey to the hype of AI without thoroughly understanding its limitations risk being blindsided by its flaws. The early testing of models like GPT-3 and GPT-4o revealed significant issues, from **hallucinations** (where AI generates false information) to **biases** baked into the data the models were trained on.

Bolton highlights that while these problems were reported early on, they were often dismissed or ignored by the broader community in the rush to embrace AI’s potential. But as we’ve seen with the steady stream of problem reports that followed, these issues couldn’t be swept under the rug forever. The lesson? **Critical thinking and skepticism are essential in the age of AI**. Those who ask tough questions, test the claims, and remain grounded in reality will be far better equipped to navigate the future than those who blindly follow the hype.

We should consider our relationship with technology. As AI continues to advance, it’s easy to become seduced by the idea that technology can solve all of our problems. Michael instead encourages us to examine our beliefs about AI and technology in greater depth and breadth.

- Are we relying on AI to do work that should be done by humans?
- Are we putting too much trust in systems that are inherently flawed?
- Are we, in our rush to innovate, sacrificing quality and safety?

Critical thinking, and actually practicing/using it, is more relevant than ever. As we explore the possibilities AI offers, we must remain alert to the risks. This is not just about preventing bugs in software—it’s literally about safeguarding the future of technology and ensuring that we use AI in ways that are ethical, responsible, and aligned with human values. 

Ultimately, testers have a vital role in this new world of AI-driven development. Testers are not just there to check that software functions as expected, this is our time to step up and be the clarions we claim we are. We are the guardians of quality, the ones who ask “What if?”, and probe the system for hidden flaws. In the age of AI, we need to be and do this more than ever.

Michael posits that AI may assist with repetitive tasks, but it cannot match the *intuition, curiosity, and insight that human testers bring to the job. 

It’s still unclear what the AI future will hold. Will we find ourselves in an AI-enhanced world of efficiency and innovation? Will our optimism give way to a more cautious approach? We don't know, but to be sure, those who practice critical thinking, explore risks, and test systems rigorously will have a genuine advantage.

The Test Automation Blueprint: A Case Study for Transforming Software Quality with Jeff Van Fleet (a PNSQC Live Blog)

Today, delivering high-quality software at speed isn’t just a goal, it’s a necessity. Whether your organization has a small Agile team or a huge corporation, creating a streamlined, efficient testing process can dramatically reduce costs and accelerate time to market. But how do you actually achieve that transformation? Jeff Van Fleet, President and CEO of Lighthouse Technologies, goes into depth with some practical tips and proven principles to guide organizations toward effective test automation. 

One of the most important steps in transforming your organization’s approach to test automation is engaging your leadership team. Test automation initiatives often require significant investment in tools, training, and process changes—investments that can only happen with leadership support. Jeff highlights the importance of showing clear ROI by presenting leaders with real-time reporting dashboards that demonstrate how automation accelerates delivery and improves quality.

These dashboards provide visibility into the success of the test automation effort, making it easy for leadership to see the value in continuing to invest. Data-driven views and knowledge keep leadership engaged and committed to long-term quality improvement.

It's a big leap from manual testing to automation. I know, I've been there! Many manual testers may feel apprehensive about making that transition. However, Jeff emphasizes that with the right training and support, manual testers can successfully transition to automation and get fully involved in the new process. Lighthouse Technologies focuses on equipping testers with the tools, skills, and confidence to tackle automation.

We have to approach this training with empathy and patience. Many manual testers bring invaluable domain expertise, which, when combined with automation skills, can significantly enhance the quality of the testing process. Investing in your existing team, instead of sidelining them, can transform teams and build a strong, motivated automation workforce.

we've pushed the idea of shift-left testing for a while now.  Many organizations are eager to adopt it, but few know how to implement it effectively. Moving testing earlier in the development cycle helps catch bugs before they snowball into more complex, costly issues.

By collaborating closely with developers to improve unit testing, teams can identify and address defects at the code level, long before they reach production. 

One of the challenges teams face is trying to implement automation while managing in-flight releases. Jeff offers practical strategies for balancing catch-up automation (automating legacy systems or current processes) with ongoing development work. His advice: start small, automate critical paths first, and build incrementally. This allows teams to gradually integrate automation without derailing existing release schedules.

Engaging with developers is another critical component of successful test automation. Often, there’s a disconnect between QA and development teams, but Lighthouse Technologies’ approach bridges that gap by partnering closely with developers throughout the testing process. By working together, developers and testers can create more effective test cases, improve unit test coverage, and ensure that automated tests are integrated seamlessly into the CI/CD pipeline.

For organizations looking to embrace test automation, the key takeaway is that it’s not just about tools—it’s about people, processes, and leadership. By following these principles, teams can accelerate their test automation efforts and create a culture of quality that drives both speed and innovation.

When Humans Tested Software (AI First Testing) with Jason Arbon (a PNSQC Live Blog)

Are we at the edge of a new era in software development—an era driven by Generative AI? Will AI fundamentally change the way software is created? As GenAI begins to generate code autonomously, with no developers in the loop, how will we test all this code?

That's a lot of bold questions, and if I have learned anything about Jason Arbon over the years, bold is an excellent description of him. To that end, Jason suggests a landscape where AI is set to generate 10 times more code at 10 times the speed, with a 100-fold increase in the software that will need to be tested. The truth is, that our traditional human-based testing approaches simply won’t scale to meet this challenge.

Just like human-created code, AI-generated code is not immune to bugs. As GenAI continues to evolve, the sheer volume of code it produces will surpass anything we’ve seen before.  Think about it: if AI can generate 10 times more code, that’s not just a productivity boost—it’s a tidal wave of new code that will need to be tested for reliability, functionality, and security. This surge is not just a matter of speed; it’s a "complexity crisis". Modern software systems, like Amazon.com, are far too intricate to be tested by human hands alone. According to Jason, AI-generated code will require AI-based testing. Not just because it’s faster, but because it’s the only solution capable of scaling to match this growth.

The current approach to software testing struggles to keep pace with traditional development cycles. In the future, with the explosion of AI-generated code, human-based testing methods will fall short unless we somehow hire a tenfold increase in software testers (I'm skeptical of that happening). Manual testing will absolutely not be able to keep up, and automated testing as we know it today won’t be able to keep up with the increasing volume and complexity of AI-generated systems.

What’s more, while GenAI can generate unit tests, it can’t test larger, more complex systems. Sure, it can handle individual components, but it stumbles when it comes to testing entire systems, especially those with many interdependencies. Complex applications, like enterprise-level platforms or global e-commerce sites, don’t fit neatly into a context window for GenAI to analyze. This is where Jason says the need for AI-based testing becomes critical.

The future isn’t just about AI generating code—it’s about AI testing that AI-generated code. According to Jason,  AI-based testing is the key to addressing the 100X increase in software complexity and volume. Only AI has the ability to scale testing efforts to match the speed and output of Generative AI. 

AI-first testing systems should be designed to:

Automate complex testing scenarios that would be impossible for traditional methods to cover efficiently.

Understand and learn from system behaviors, analyzing patterns and predicting potential failures in ways that humans or current automated tools cannot.

Adapt and evolve, much like the AI that generates code, enabling continuous testing in real-time, as software systems grow and change.

As Jason points out, AI is not a fad or a trend, it’s the only way forward. As we move into an era where Generative AI produces vast amounts of code at breakneck speed, AI-based testing will be the way that we help ensure that the software we create tomorrow will be reliable, functional, and secure.

As a Constellation We Shine: Mentorship Through the Lens of Shine Theory with Sophia McKeever (A PNSQC Live Blog)

The traditional view of mentorship often paints a one-sided picture—an experienced mentor guiding a mentee, offering advice, and providing support. What if mentorship could be something more? In Sophia McKeever’s talk, “As a Constellation We Shine,” she explores how mentorship, when approached through the principles of Shine Theory, becomes a powerful two-way relationship where both mentor and mentee grow and thrive together.

At its core, mentorship is about growth. But too often, we think of that growth as a one-way transfer of knowledge, with the mentor imparting wisdom to a less experienced mentee. Shine Theory transforms this into a mutual exchange of knowledge, energy, and growth.

Shine Theory, coined by Aminatou Sow and Ann Friedman, is the idea that “I don’t shine if you don’t shine.” In the context of mentorship, this means that both the mentor and the mentee invest in each other’s success. By supporting one another, both individuals flourish, creating a "constellation" of talent, insight, and progress that benefits not just the participants but their entire community.

When mentors and mentees mutually invest in each other, their relationship evolves into something far more enriching than a simple exchange of information. Shine Theory encourages both parties to approach the relationship with empathy and intent, focusing on how they can elevate one another.

For example, a mentor may share technical expertise and career advice, while the mentee offers fresh perspectives, creativity, and a deeper understanding of new technologies. Reciprocity ensures that both parties benefit, no matter their level of experience or their role within the industry.

Sophia shares several anecdotes of her own journey as a testament to the power of mutual investment. A self-taught SDET, she has navigated a dynamic career in tech, moving from customer service to key roles at Microsoft,  Apple, and now at Pokemon. Along the way, she has found that her most rewarding mentorship experiences were those where she could both teach and learn, growing alongside her mentees as they collaborated and supported one another.

When mentorship is approached through the lens of Shine Theory, it becomes more than just career development—it becomes a source of mutual enrichment and excitement. Sophia highlights several key ways Shine Theory can elevate mentorship:

Fostering a Growth Mindset: Both mentor and mentee approach the relationship with the mindset that they can learn from each other. This creates a more dynamic and creative exchange of ideas.

Building Empathy: Mentorship based on Shine Theory encourages empathy, as both individuals invest emotionally in the other’s success. This empathy fosters deeper connections and a stronger sense of community.

Celebrating Mutual Wins: When one person shines, both shine. Mentorship becomes less about individual achievements and more about shared success, creating a supportive environment where both mentor and mentee can thrive.

Breaking Down Hierarchies: Shine Theory shifts the traditional power dynamic of mentorship, making it less about hierarchy and more about collaboration. It doesn’t matter if one person has more experience or a higher title—both are equals in the relationship, learning and growing together.

Mentorship isn’t just about guiding someone along their career path. It’s about "creating a constellation", where both mentor and mentee shine together, contributing to each other’s success and elevating their collective potential. In today’s fast-paced tech world, where innovation and growth often come from unexpected places, this approach to mentorship is more relevant than ever. By embracing Shine Theory, mentors and mentees can build meaningful, two-way relationships that not only enhance their individual careers but also help to develop a more inclusive, supportive community.

Our Keynote is Finished, and I have an Announcement To Make

 Today I had the chance to deliver my first keynote talk at a conference. Matt Heusser and I delivered a talk about "AI in Testing: Hip of Hype?" and by all accounts, I think it went well. We set up the talk to play off each other, where I represented the hip elements of AI, and Matt highlighted the Hype aspects. At times it may have come across as a bit of an Abbott and Costello routine but that added to the fun of it for me. I will do a more in-depth post on our keynote later but I did make an announcement here that needs to be broadcast.

On October 1, 2024, I started working as a Senior Development Test Engineer for ModelOP. They are based in Chicago and are focused on providing monitoring and management solutions for AI Governance. If that seems vague, it's because I'm literally learning about all this as I go. My job responsibilities will be testing-related, with a major emphasis on automation and accessibility. 

We made a point in this talk that this would be the last of the series of times Matt and I have taught together or spoken together where I was working specifically for Excelon Development. Matt gave me many valuable insights into what it took to be an independent consultant and how to work effectively in that space. I hope to leverage those lessons in this new role and ultimately be effective in that capacity.

It's been a strange journey over the past fifteen and a half months but I learned a lot through it, I think I grew a great deal, and I learned I had capacity in areas I didn't think I had.

Humanizing AI with Tariq King (a PNSQC Live Blog)

I've always found Tariq's talks to be fascinating and profound and this time around we're going into some wild territory.

AI is evolving, and with each new development, it’s becoming more "human". It’s not just about executing tasks or analyzing data—it’s about how AI communicates, adapts, and even imitates.

So AI is becoming human... in how it communicates. That's a big statement but with that qualifier, it is more understandable. AI is no longer a cold, mechanical presence in our lives. Today’s AI can respond based on context, understanding the tone of requests and adjusting replies accordingly. It can mimic human conversation, match our language, and create interactions that feel amazingly real. Whether you’re chatting with a customer service bot or getting personalized recommendations, AI can engage with us in ways that were once the domain of humans alone.

Okay, so if we are willing to say that AI is "becoming human", how should we shape these interactions?What should the boundaries be for AI communication, and how do we ensure it serves us, rather than replaces us?

Beyond just communication, AI is showing remarkable creativity. AI can now write stories, compose music, and generate art, ranging from wild and weird to quite stunning (I've played around with these for several years, and I have personally seen the development of these capabilities and they have indeed become formidable and impressive). What once seemed like the exclusive realm of human creativity is now being shared with machines. AI is no longer just a tool—it’s being used as a collaborator that can generate solutions and creative works that blur the line between human and machine-generated content.

Tariq points out that this raises some significant and critical questions.  Who owns AI output? How do we credit or cite AI authorship? How do we confirm the originality of works? Perhaps more to the point, as AI generates content, what is the human role in the creative process? And how do we ensure that the human element remains at the forefront of innovation?

AI is getting better at how convincingly it can imitate humans. But there’s a caveat: AI is prone to hallucinations, meaning it can produce plausible and relatable material that feels right for the most part but may be wrong (and often is wrong). I have likened this in conversations to having what I call the "tin foil moment". If you have ever eaten a food truck burrito (or any burrito to go, really) you are familiar with the foil wrapping. That foil wrapping can sometimes get tucked into the folds and rolls of the burrito. Occasionally, we bite into that tin foil piece and once we do, oh do we recognize that we have done that (sometimes with great grimacing and displeasure). Thus, when I am reading AI-generated content, much of the time, I have that "tin foil" moment and that takes me out of believing it is human (and often stops me being willing to read what follows, sadly).

The challenge here is not just humanization. We need to have critical oversight over it so that we can have it do what we want it to do and not go off the rails. How do we prevent AI from spreading misinformation? And how can we design systems that help us discern fact from fiction in a world where AI-generated content is increasingly common?

Okay, so we are humanizing AI... this begs a question... "Is this something we will appreciate or is it something that we will fear?" I'm on the fence a bit. I find a lot of the technology fascinating but I am also aware of the fact that humanity is subject to avarice and mendacity. Do we want AI to be subject to it as well, or worse, actively practice it? What unintended consequences might we see or incur? 

For some of you out there, you may already be thinking of some abstract idea called "AI Governance", which is the act of putting guardrails and safety precautions around AI models so that they perform as we want them to. This means setting clear ethical guidelines, robust oversight mechanisms, and working to ensure that AI is used in ways that benefit society. More to the point, we need to continuously monitor and work with AI to help ensure that the data that it works with is clean, well-structured, and not poisoned. That is a never-ending process and one we have to be diligent and mindful of if we wish to be successful with it. 

Make no mistake,  AI will continue to evolve. To that end, we should approach it with both excitement and caution. AI’s ability to communicate, create, and imitate like humans presents incredible opportunities, but it also brings with it significant challenges. Whether AI becomes an ally or a threat depends on how we manage its "humanization".

AI-Augmented Testing: How Generative AI and Prompt Engineering Turn Testers into Superheroes, Not Replace Them with Jonathon Wright’s (a PNSQC Live Blog)

Sad that Jonathon couldn't be here this year as I had a great time talking with him last year but since he was presenting remotely, I could still hear him talking on what is honestly the most fun title of the entire event (well played, Jonathon, well played ;) ).

It would certainly be neat if AI was able to enhance our testing prowess, helping us find bugs in the most unexpected places, and create comprehensive test cases that could cover every conceivable scenario (editors note: you all know how I feel about test cases but be that as it may, many places value and mandate them, so I don't begrudge this attitude at all).

Jonathon is calling for us to recognize and use "AI-augmented testing" where AI doesn't replace testers but instead amplifies their capabilities and creativity. Prompt engineering can elevate the role of testers from routine task-doers to strategic innovators. Rather than simply executing tests, testers become problem solvers, equipped with "AI companions" that help them work smarter, faster, and more creatively (I'm sorry but I'm getting a "Chobits" flashback with that pronouncement. If you don't get that, no worries. If you do get that, you're welcome/I'm sorry ;) (LOL!) ).

The whole goal of AI-augmented testing is to elevate the role of testers. Testers are often tasked with running manual or automated tests, getting bogged down in repetitive tasks that demand "attention to detail" but do not allow much creativity or strategic thinking. The goal of AI is to "automate the routine stuff" so we can "allowing testers to focus on more complex challenges" ("Stop me! Oh! Oh! Oh! Stop me... Stop me if you think that you've heard this one before!") No disrespect to Jonathon. whatsoever, it's just that this has been the promise for 30+ years (and no, I'm not going to start singing When In Rome to you, but if that earworm is in your head now.... mwa ha ha ha ha ;) ).

AI-augmented testing is supposed to enable testers to become strategic partners within development teams, contributing, not merely bug detection but actual problem-solving and quality improvement. With AI handling repetitive tasks, testers can shift their attention to more creative aspects of testing, such as designing unique test scenarios, exploring edge cases, and ensuring comprehensive coverage across diverse environments. This shift is meant to enhance the value that testers bring to the table and make their roles more dynamic and fulfilling. Again, this has been a promise for many years, maybe there's some headway here.

The point is that testers who want to harness the power of AI will need a roadmap for mastering AI-driven technologies. there are many of them out there and there is a plethora of options in a variety of implementations from LLMs to dedicated testing tools. No tester will ever master them all but even if you only have access to a LLM system like Chat GPT, there is a lot that can be done with Prompt Engineering and harnessing the output of these LLM systems. They are of course not perfect but they are getting better and better all the time. AI can process vast amounts of data, analyze patterns, and predict potential points of failure, but it still requires humans to interpret results, make informed decisions, and steer the testing process in the right direction. Testers who embrace AI-augmented testing will find themselves better equipped to tackle the challenges of modern software development. In short, AI will not take your job... but a tester who is well-versed in AI just might.

This brings us to Prompt engineering. This is the process of precise, well-designed prompts that can guide generative AI TO perform specific testing tasks. Mastering prompt engineering will allow testers to customize AI outputs to their exact needs, unlocking new dimensions of creativity in testing.

Ss What Can we Do With Prompt Engineering? We can use it to...

-  instruct AI to generate test cases for edge conditions
- simulate rare user behaviors
- explore vulnerabilities in ways that would be difficult or time-consuming to code manually.
- validating AI outputs so that we ensure that generated tests align with real-world needs and requirements.

Okay, so AI can act as a trusted companion—an ally helping testers do their jobs more effectively, without replacing the uniquely human elements of critical thinking and problem-solving. Wright’s presentation provides testers with actionable strategies to bring AI-augmented testing to life, from learning the nuances of prompt engineering to embracing the new role of testers as strategic thinkers within development teams. We can transform workflows so they are more productive, efficient, and engaging. 

I'll be frank, this sounds rosy and optimistic but wow, wouldn't it be nice? The cynic in me is a tad bit skeptical but anyone who knows me knows I'm an optimistic cynic. Even if this promise turns out to be a magnitude of two less than what is promised here... that's still pretty rad :).

Vulnerabilities in Deep Learning Language Models (DLLMs) with Jon Cvetko (A PNSQC Live Blog)

Vulnerabilities in Deep Learning Language Models (DLLMs)

There's no question that AI has become a huge topic in the tech sphere in the past few years. It's prevalent in the talks that are being presented at PNSQC (it's even part of my talk tomorrow ;) ). The excitement is contagious, no doubt exciting but there's a bigger question we should be asking (and John Cvetko is addressing)... what vulnerabilities are we going to be dealing with, specifically in Deep Learning Language Model Platforms like ChatGPT?

TL;DR version: are there security risks? Yep! Specifically, we are looking at Generative Pre-trained Transformer (GPT) models. As these models evolve and expand their capabilities, they also widen the attack surface, creating new avenues for hackers and bad actors. It's one thing to know there are vulnerabilities, it's another to understand them and learn how to mitigate them.

Let's consider the overall life cycle of a DLLM. we start with our initial training phase, then move to deployment, and then monitor its ongoing use in production environments. DLLMs require vast amounts of data for training. What d we do when this data includes sensitive or proprietary information? If that data is compromised,  organizations can suffer significant privacy and security breaches.

John makes a point that federated training is growing when it comes to the development of deep learning models. Federated training means multiple entities will contribute data to train a single model. The benefit is that it can distribute learning and reduce the need for centralized data storage, it also introduces a new range of security challenges. Federated training increases the risk of data poisoning, where malicious actors intentionally introduce harmful data into the training set to manipulate the model’s generated content.

Federated training decentralizes the training process so that organizations can develop sophisticated AI models without sharing raw data. However, according to Cvetko, a decentralized approach also expands the attack surface. Distributed systems are nearly by design more vulnerable to tampering. Without proper controls, DLLMs can be compromised before they even reach production.

there is always a danger of adversarial attacks during training. Bad actors could introduce skewed or intentionally biased data to alter the behavior of the model. This can lead to unpredictable or dangerous outcomes when the model is deployed. These types of attacks can be difficult to detect because they occur early in the model’s life cycle, often before serious testing begins.

OK, so that's great... and unnerving. We can make problems for servers. So what can we do about it? 

Data Validation: Implement strict data validation processes to ensure that training data is clean, accurate, and free from malicious intent. By scrutinizing the data that enters the model, organizations can reduce the risk of data poisoning.

Model Auditing: Continuous monitoring and auditing of models during both training and deployment phases. This helps detect oddities in the model behavior early on, allowing for quicker fixes and updates.

Federated Learning Controls: Establish security controls around federated learning processes, such as encrypted communication between participants, strict access controls, and verification of data provenance.

Adversarial Testing: Conduct adversarial tests to identify how DLLMs respond to unexpected inputs or malicious data. These tests can help organizations understand the model’s weaknesses and prepare for potential exploitation.

There is a need today, for "Responsible AI development." DLLMs are immensely powerful and can carry significant risk potential if not properly secured. While this "new frontier" is fun and exciting, we have a bunch of new security challenges to deal with. AI innovation does not have to come at the expense of security. By understanding the life cycle of DLLMs and implementing the right countermeasures, we can leverage the power of AI while at the same time safeguarding our systems from evolving threats.

Mistakes I Made So You Don’t Have To: Lessons in Mentorship with Rachel Kibler (A PNSQC Live Blog)

I have known Rachel for several years so it was quite fun to sit in on this session and hear about struggles I recognized all to well. I have tried training testers over the years, some I've been successful with, others not so much. When a new tester comes along quickly, seems to get it, and digs testing, that's the ultimate feeling (well, *an* ultimate feeling). 

However, as Rachel points out, it’s also full of potential missteps, and as she said clearly at the beginning, "Believe me, I’ve made plenty!" This was a candid and honest reflection of what it takes to be a mentor and help others who are interested in becoming testers, as well as those who may not really want to become testers, but we mentor them anyway.

We can sum this whole session up really quickly with "Learning from our mistakes is what makes us better mentors—and better humans"... but what's the fun in that ;)?

Mistake 1: One-Size-Fits-All Training Doesn’t Work

There is no single, ideal method to teach testing that would work for everyone. Rachel had clear plans and expected to get consistent results. However, "people are not vending machines". You can’t just input the same words and expect identical outcomes. Each person learns differently, has different experiences, and responds to unique challenges.

Mistake 2: Setting the Wrong Challenges

It's possible to give team members tasks that are either too difficult or too easy, failing to gauge their current abilities. The result? Either they are overwhelmed and lost confidence, or they felt under-challenged and disengaged. Tailoring challenges to a trainee’s current skill level not only builds their confidence but also keeps them engaged and motivated. As mentors, our role is to provide enough support to help them succeed while still pushing them to grow.

Mistake 3: Don't Forget the Human Element

At the end of the day, we’re working with humans. Rachel’s talk highlights the importance of remembering that training isn’t just about passing on technical knowledge—it’s about building relationships.  Everyone has unique needs, emotions, and motivations. By focusing on the human element, we can create an environment where people feel supported and valued, making them more likely to succeed.

Mistake 4: Not Embracing Mistakes as Learning Opportunities

Mistakes are opportunities to learn. Mistakes aren’t failures—they’re stepping stones. Whether it’s a trainee misunderstanding a concept or a mentor misjudging a situation, these moments are chances to grow. They teach us humility, patience, and resilience.

Rachel’s talk is a reminder that no one is a perfect mentor right out of the gate. The process of becoming a great mentor is filled with trial and error, reflection, and growth. Also, Imposter Syndrome is very real and it can be a doozy to overcome.  Ultimately, the key takeaway is this: mentorship is a journey, not a destination. We will make mistakes along the way, but those mistakes will help shape us into more effective, empathetic, and responsive mentors.

Scaling Tech Work While Maintaining Quality: Why Community is the Key with Katherine Payson (a PNSQC Live Blog)

If someone had told me ten years ago I'd be an active member of the "gig economy" I would have thought they were crazy (and maybe looked at them quizzically because I wouldn't entirely understand what that actually meant. in 2024? Oh, I understand it, way more than I may have ever wanted to (LOL!). Rather than. looking at this as a bad thing, I'm going to "Shift Out" (as Jon Bach suggested in the last talk) and consider some aspects of the gig economy that are helping to build and scale work and, dare we say it, quality initiatives. 

Katherine Payson offers some interesting perspectives:

- The gig economy generates $204 billion globally
- many companies are leveraging and taking advantage of this, including international companies hiring all over the world for specific needs (I know, I did exactly this during 2024)
- In 2023, the anticipated growth rate for gig work was expected to be 17%
- By 2027 the United States is expected to have more gig workers than traditional full-time employees

This brings up an interesting question... with more people involved in gig work, and not necessarily tied to or beholden to a company for any meaningful reasons, how do these initiatives scale, and how do quality and integrity apply?

Strong Community is the approach that Katherine is using and experiencing over at Cobalt, a company that specializes in "pentesting-as-a-service". Cobalt has grown its pool of freelance tech workers to over 400 in three years. That's a lot of people in non-traditional employment roles. So what does that mean? How is trust maintained? How is quality maintained? Ultimately, as Katherine says, it comes down to effective "Community Building".

Today, many businesses are looking for specialized skills, frequently beyond what traditional full-time employees and employment can do. Yes, AI is part of this shift but there is still a significant need for human expertise. As Cobalt points out, cybersecurity, software development, and other technical fields definitely still require human employees with a very human element to them. What this means is that there is a large rise in freelance professionals actively offering niche talents on a flexible, on-demand basis (likely also on an as-needed basis both for the companies and the gig workers themselves). Again, the bigger question is "Why should a gig worker really care about what a company wants or needs?"

Community can be fostered directly when everyone is in the same town, working on the same street, going to the same office. When Cobalt first began scaling, they relied on a traditional trust model that worked well for a smaller, more centralized team. As the number of freelancers grew, however, this model began to show its limitations. Without a more robust system in place, it would be impossible to ensure consistent quality across a distributed workforce.

Tools can go a certain distance when it comes to helping manage quality and production integrity but more to the point, developing actual communities within organizations is another method for helping develop quality initiatives that resonate with people from all involvements in their organizations.

Cobalt prides itself as a company that is able to maintain quality at scale. It claims to create a culture where freelancers feel connected, supported, and motivated to deliver their best work. So how does Cobalt do that?

Collaboration and Communication: Freelancers can work independently, but they don't work in isolation. Cobalt believes in open communication, where freelancers can collaborate with one another, share knowledge, and learn from each other’s experiences.

Mentorship and Professional Development: Cobalt invests in the professional growth of freelancers. Mentorship opportunities, training programs, and access to industry resources help their freelance community continuously hone their skills.

Recognition and Incentives: High-performing freelancers are recognized and rewarded for their contributions. This helps retain top talent and encourages others to aim for top-quality work.

Feedback Loop: Freelancers receive regular feedback on their work, helping them improve and keep quality high across the board.

As the gig economy continues to grow, maintaining quality at scale will become increasingly important everywhere. Cobalt aims to embrace the strengths of their freelance workforce, not just as individual contributors but as part of a larger community. Scaling with freelancers is not just about hiring more people—it’s about building a culture of collaboration, growth, and trust. To ensure quality remains front and center, companies need to invest in their communities every bit as much as much as they do in their tools and processes.

Shifting Out: Beyond Left and Right in DevOps with Jon Bach (a PNSQC Live Blog)

If you have any involvement in testing or DevOps we hear a bunch about Shift-Left and Shift-Right. The idea is that we bring testing earlier in the development of the product and that we continue to test after the product is released. But what if there’s a third dimension, one that helps us make sure we are testing where most effective and needed, along with making the most of our testing resources? Jon Bach wants us to consider "Shifting Out" along with the familiar options.

Think of Shifting Out less as a directional movement and more of an elevation movement (rise up or levitate might be better words but it's not as memorable as Shift Out, so I get it :) If shift-Left deals with working with or around a particular tree, Shift Out gives you a view of the entire forest. Shifting out is all about your perspective, and along with that, balancing testing resources so that we have placed our attention on the important areas (harder to see when staring at one tree, so to speak ;) ). By logic, we can also consider Shifting In after we have Shifted Out.

Okay, so I get the general idea of Shift Out. How do we do it?

Customer bug reports are an interesting example. Who owns the issue? Where should testing be applied? When? Would Shift-Left or Shift-Right be helpful here? Does it even fall within the Left or Right framework? As we shift out, we start to see that problems don’t always fit neatly into one category—they often require collaboration across multiple teams to resolve. 

Early bug detection is great but the fact is that we can’t always catch bugs early, no matter how much we Shift-Left. Incomplete requirements, unknown user behaviors, and system complexity introduce a lot of unknowns, meaning that some issues are not only unlikely to be found but may be impossible to find until the code goes live. Shifting out acknowledges this and encourages teams to plan for uncertainty rather than simply striving to eliminate it.

What signals are your tests sending? Meaning are your tests highlighting the right problems? What can you learn from the data generated across development, test, and production environments? It's not enough to just run the tests. We need to analyze what the tests are telling us and what data we are getting and analyzing. Additionally, it may make sense to reframe existing tests. This way, we get the information from our original tests but also get additional information by pivoting to a different aspect or need. Same test, with different results, and possibly different conclusions.

Shifting-Out (and Shifting-In) are all about zooming in and out to consider the broader context of our testing strategies. Shift-Left and Shift-Right help focus on specific aspects of the software lifecycle, shifting out lets us step back and see how the pieces fit together. 

Shift OUT is about:

Outlook: getting credible action items and information
Understanding: knowing about the issues and contexts where they fit and what/why it matters
Treatment: knowing where and when the appropriate solution needs to be applied, as well as why it is the most effective

Ultimately, focusing solely on Shift-Left or Shift-Right leads to a tunnel vision effect. Shifting-Out helps with developing perspectives for managing all of the possible quality processes. Catching bugs earlier and learning from production is important. So is understanding the bigger picture and making informed decisions that help balance both.