We are thrilled to welcome Millan Kaul as an invited speaker!

Millan is a Quality Engineering leader with global experience building engineering-first quality cultures across startups, scale-ups, and enterprises on four continents. Spanning domains like banking, IoT, fuel analytics, and payments, his focus centers on scaling teams, driving business-aligned outcomes, and owning the Total Cost of Ownership (TCO) for quality to balance speed with control.

In recent years, Millan has spearheaded practical AI initiative: running local LLMs, engineering MCP-based agents, and implementing trusted, organization-level guardrails. A regular global conference speaker, he frequently shares his ongoing work and practical technical insights on his blog, Quality With Millan.

Keynote Session Details

• Topic: From experimenting with AI to owning its total cost: What leaders often underestimate

• What to Expect: As AI accelerates code generation, output increases—but certainty often drops. Moving past the initial hype, Millan will explore what happens when AI scales in production environments. Drawing from hands-on experience running local models and MCP-based systems, he will lead an interactive conversation on managing AI’s total cost of ownership (TCO)—including infrastructure, signal quality, team decision-making, and responsible leadership.

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New Sponsor: TestDino

Sponsored Content | From our Community Partner: TestDino

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Panels at PNSQC

Get ready for deep-dive discussions and honest, practical insights from industry leaders. We are excited to announce three featured panels bringing real-world experience to the biggest challenges in quality engineering today:

• 🤖 AI in the Testing Room: Real Results, Real Limits

  Beyond the buzz, where is AI actually delivering value in quality assurance—and where does it fall short? Join practitioners as they separate hype from reality, discussing the practical impacts, hidden maintenance costs, and realistic boundaries of integrating AI into modern test strategies.

• 📈 Quality at Scale: Building Test Programs That Actually Work

  Scaling software delivery shouldn’t mean scaling chaos. This panel examines how teams build sustainable quality programs that keep pace with rapid releases, maintain reliable signal quality across environments, and foster team-wide quality ownership.

• 🌐 Quality Beyond the Screen: Testing Devices, Systems, and the Real World

  Software doesn’t live in isolation. Explore the unique quality and testing challenges of complex ecosystems—from IoT devices and embedded systems to hardware-software integration and real-world edge environments.

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Validation is no longer just about proving that a chip meets its specification. Today’s AI data centers are living ecosystems where liquid-cooled racks, heterogeneous accelerators, NVMe and CXL fabrics, firmware, operating systems, drivers, Kubernetes clusters, AI services, and autonomous agents continuously interact. CI/CD pipelines no longer deploy only software; they orchestrate firmware updates, infrastructure changes, validation workloads, and increasingly, AI-driven decision-making.

As these systems become more interconnected and autonomous, one question keeps surfacing in engineering labs and quality organizations:

What does “good” look like when validation spans hardware, software, and autonomous tooling?

For years, the industry has measured success through volume: more test cases, more automation, more dashboards, and higher pass percentages. While these metrics are useful operational indicators, they rarely answer the question that engineering leaders care about most:

How much confidence do we actually have in shipping this system?

A platform may execute hundreds of thousands of tests with a 98% pass rate and still experience failures caused by interactions between firmware and drivers, memory and networking, storage and AI workloads, or autonomous agents making incorrect operational decisions. In AI infrastructure, failures increasingly occur at the boundaries between components rather than within the components themselves.

It is time to redefine what “good” means.

Instead of equating good validation with execution volume, we should equate it with engineering maturity, our ability to demonstrate, through measurable evidence, that validation activities reduce system risk and increase operational reliability.

For post-silicon and hardware validation engineers, this means moving beyond heroic bringup efforts and custom scripts toward explicit validation strategies, clearly defined exit criteria, and orchestration patterns that survive multiple product generations and organizational change. For software quality leaders, it means treating AI agents, CI/CD pipelines, and automation frameworks as engineering systems that must earn trust through quantitative evidence. Agent reliability, safety compliance, delegation accuracy, traceability, and operational cost should become first-class quality metrics rather than afterthoughts.

A Practical Framework for Engineering Confidence

To move from aspiration to execution, AI data center validation can be viewed through

three complementary pillars.

1. Validation Architecture: Validation should begin with an intentional architecture rather than an accumulation of test suites. The architecture defines how hardware, firmware, operating systems, drivers, AI frameworks, cloud infrastructure, and autonomous agents interact throughout the validation lifecycle. It identifies ownership of engineering risks, structures CI/CD quality

   gates, connects lab orchestration with production telemetry, and ensures that validation is designed around system interactions, not isolated components.

   Instead of asking, “Did every component pass?”, organizations should ask:

   “Did we validate every critical interaction across the system?”

2. Engineering Validation Maturity (EVM): Architecture alone is insufficient without organizational discipline. An Engineering Validation Maturity (EVM) model provides a structured way to evaluate how consistently validation practices are applied across teams. Inspired by process maturity frameworks such as TMMi but adapted for AI infrastructure and post-silicon environments, EVM focuses on engineering capability rather than compliance.

   Rather than measuring how many tests exist, maturity evaluates whether organizations have:

• Shared validation strategies

• Repeatable engineering processes

• Risk-based validation planning

• System level interaction coverage

• Continuous feedback from production

• Quantitative evidence supporting release decisions

Maturity shifts the conversation from “Are we finished testing?” to:

“How mature is our validation capability?”

3. Engineering Validation Confidence Score (EVCS): Engineering leaders also need an executive-level measure that communicates confidence, not simply activity. An Engineering Validation Confidence Score (EVCS) combines multiple dimensions into a single engineering health indicator. Rather than reporting only test pass rates or automation percentages, EVCS evaluates factors such as:

• Risk coverage across the platform

• Reliability confidence

• Recovery and resiliency validation

• AI agent trustworthiness and safety

• Observability and diagnostic capability

• Defect leakage trends

• Cost efficiency of validation activities

• Speed at which production incidents become new validation assets

An EVCS of 91 out of 100 communicates far more than “98% of tests passed.” It demonstrates whether validation has meaningfully reduced engineering risk and increased confidence in the platform.

From Automation to Measured Reliability

Good AI data center validation is not defined by statements like:

“We automated the lab.”

or

“We integrated AI into our testing.”

Instead, good validation can confidently say:

This requires evaluating autonomous agents with the same rigor applied to firmware and software, validating cost and capacity guardrails alongside performance, and ensuring that production telemetry continuously improves future validation strategies. Validation becomes a living engineering system, where development, validation, deployment, telemetry, and failure learning form a continuous feedback loop. When validation operates through architecture, maturity, and measurable confidence, innovation stops being a collection of impressive tools and becomes an engineering capability that consistently transforms complexity into reliability.

Looking Ahead

The future of post-silicon validation will not be defined by larger test suites or increasingly sophisticated AI tools. It will be defined by our ability to engineer confidence across an evolving ecosystem of hardware, firmware, software, cloud infrastructure, and intelligent agents.

The next generation of quality leaders, whether they come from hardware labs or software QA, will not be recognized for automating more tests. They will be recognized for building validation ecosystems that continuously demonstrate engineering confidence, turning innovation into reliability that can be measured, explained, and trusted.

These concepts build on ongoing research into agent evaluation, agent security test maturity, NVMe validation, and FinOps aligned quality frameworks. Readers interested in detailed models, metrics, and case studies are welcome to refer to my recent publications in these areas, which explore how to implement Engineering Validation

Maturity and Engineering Validation Confidence in real AI data center and post-silicon environments. Selected papers are available on my ResearchGate profile, and I am open to collaborating with other practitioners and researchers who want to advance this work through joint studies, pilots, or future publications.

About the Author:

Lakshmi Vidya Peri is an operations-focused validation engineering and quality transformation leader with over a decade of experience in server, storage, and data center validation. She has led NVMe qualification programs and modernized test orchestration and Lab-as-a-Service capabilities, tripling throughput and reducing time-to-market by 30 percent. Her work spans lab operations, software procurement rationalization, KPI architecture, and Voice-of-Customer driven improvement, often acting as a liaison between engineering, operations, and customer insights.

Her research at Boise State University explored improving ARM Cortex-A8 performance through victim-cache simulation and shift-left pre-silicon SoC modeling, and she continually learns and applies new methods to bring clarity to chaotic systems and align teams around evidence and outcomes. Lakshmi is a TMMi Certified Professional (TMMi-P) and holds additional certifications in marketing analytics, statistics and data science, data center virtualization, organizational leadership, product management, data governance, and cybersecurity awareness. She serves on the TMMi America board, focusing on test maturity, governance, and AI-enabled quality engineering.

Ready to map out your PNSQC 2026 journey? Our Conference-at-a-Glance is officially live!

What you’ll gain from this year’s sessions:

• AI Testing Playbooks: Practical strategies for test engineers integrating AI workflows into daily QA without sacrificing human oversight.

• Governance & Intelligence: Data-driven insights on building resilient systems and implementing intelligence-led quality governance.

• Emerging Tech & Community: Opportunities to engage with invited speakers, poster presenters, and industry leaders across three action-packed days.

• Hands-On Learning: Dedicated workshops, technical session tracks, and early-access community networking.

Don’t miss a single track! Explore the full schedule and plan your experience at https://pnsqc.org/conference/2026/at-a-glance/

Speaker Spotlight: Anna Sharpe

We are thrilled to welcome back Anna Sharpe to the PNSQC stage! Anna holds a B.S. in Computer Science from Whitworth University and began her journey in quality assurance as a test engineering intern in 2012. Today, she works remotely out of Sacramento, CA, specializing in healthcare software, while dedicating her free time to building websites for non-profits. As a multi-year PNSQC attendee, poster presenter, and now three-time conference speaker, Anna is deeply committed to expanding her testing toolkit and sharing practical lessons learned with the community.

Session Preview: Living with AI: A Test Engineer’s First-Year Journey

While AI has fundamentally changed daily testing workflows, human expertise remains irreplaceable. Drawing from her first full year using AI tools like Cursor, Anna shares early pitfalls to avoid, practical career lessons, and strategies for test engineers to stay essential and thrive alongside emerging tech.

Join us for the PNSQC Innovation Day

On Sunday, October 11, the day before the PNSQC Conference begins, PNSQC will host its first Innovation Day. Innovation Day combines student projects, robotics demonstrations, and educational seminars into a shared experience. Students can showcase their ideas and accomplishments, while parents, educators, and community members gain practical insights into Artificial Intelligence, emerging technologies, and the changing world around us. If you’re a software or hardware professional, please join us to share your experience and expertise with students. If you’re interested in volunteering for the event, please email john.cvetko@pnsqc.org

Save the Date! October 11th from 1-5:30

As a tester, I find myself exploratory testing every piece of software upon which I place my hands. I can’t help it; it is innately part of my mindset. Also, as a tester, I have championed the customer in every one of my test projects. Therefore, it would come as no surprise that much of my personal exploratory testing is done with a heavy customer experience focus. Some of my recent and most interesting exploratory tests were on Software as a Medical Device, (SaMD) products.

Having tested, managed and consulted on the verification and validation of SaMD, I understand and have managed the complexities of testing FDA-regulated software. As I informally exploratory testing these products, I’m learning how very important testing the client interaction truly is.

Although most all medical devices require some level of interaction with the customer, SaMD requires a different and broader type of interaction; the client is interacting with both the software aspects as well as the medical aspects of the product.

Although the FDA definition of software as a medical device is “Software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device.”, it is often delivered in the same ways as most software products.

When a client “meets” most SaMD products, they interact with the software in all of the usual ways that they interact with any software package. This includes the mode of delivery, usually cloud based, the device(s) upon which the software is used, perhaps a tablet, a smart phone or even a laptop. In addition, there may be devices that attach to the body or scanners to monitor body movement. Finally, there are usually some means of communication established for the client to interact with the supervising medical professional, perhaps by text or video chat. All of these must be kept in full working order, fully charged with ongoing software updates so that the clients benefit from the SaMD’s intended use and features.

It is imperative that these be thoroughly tested from the perspective of the clients who will be using the SaMD as they impact not only the user experience; but more importantly, the delivery of the medical benefit.

For example, I as I interacted with a SaMD whose intended use and features were to provide physical therapy, I found that the sensors attached to my body sometimes fail to register how I am executing the exercises. Sometimes, they fail to register movement at all. Other times, it will register movement or alignment incorrectly.

After many sessions (tests) I find that when this usually happens it is because the sensors are not fully charged. However, other times, it happens after the software receives updates.

As a client, I find it exceedingly frustrating when this happens. Sometimes, I’m unable to complete the exercise program and I don’t get the full benefit. Furthermore, if I get too discouraged with the failures, I may discontinue my compliance with the program.

Since the behaviors of the hardware and mode of delivery of the SaMD can impact how the user benefits from the SaMD’s medical intended use and features, it is critical to test the user experience.

About the Author

Gerie Owen is a Quality Engineering Manager, QE and Agile Process Expert, and an accomplished author and international conference presenter. With extensive expertise leading test teams across healthcare, financial services, transportation, utilities, and insurance, Gerie specializes in analyzing test processes, coordinating global teams across time zones, and delivering complex, high-quality projects on time and within budget.

She is passionate about mentoring Quality Engineers, coaching them in both technical testing and career advancement. A frequent keynote speaker and award-winning author—including winning Best Paper at SEETEST—Gerie has presented at major conferences worldwide (EuroSTAR, Agile Testing Days, STAREAST, STARWEST, PNSQC) and numerous DevOpsDays events. Her presentations and articles draw directly from her real-world technical experiences and process-improvement insights.

Connect with Gerie via email, X (Twitter), or LinkedIn.

The PNSQC 2026 program is nearly set! Join us October 12–14 in Portland. Here is what the week looks like:

Four Tracks:

• Quality Engineering and Tools: The technical work of testing in an AI-assisted environment.

• Emerging Technologies and AI: What agentic systems mean for quality, accountability, and trust.

• Organizational Quality and Leadership: Building and leading QA teams through disruption.

• Hardware, Safety, and Physical Systems: Quality in aerospace, defense, and safety-critical environments.

A few sessions worth knowing about that we have not mentioned before:

Nikhil Bhandari | Principal Engineer

Governing Autonomous Test Automation: From Reactive to Proactive Quality Assurance

AI-generated tests are fast, but they can be wrong in ways that compound quietly. Nikhil is building governance frameworks to catch those errors before they become costly.

Swati Seela | Director of Quality Engineering

Leading Testing in the Age of AI: Strategies for QA Teams

Addressing the management layer most conference programs skip: what does it actually mean to lead a QA team when half your tooling is being rewritten?

Yunhao Jiao | Senior Software Engineer

From Autopilot to Autonomous: Rethinking Software Quality in the Age of AI Agents

The shift from deterministic to probabilistic systems isn’t just a tooling issue—it’s a fundamental quality philosophy problem. Yunhao explores what that transition truly requires.

With 44 peer-reviewed sessions total, keynotes from Jonathon Wright, Maaret Pyhäjärvi, and Anna Sharpe, PNSQC remains nonprofit, all-volunteer, and driven by no vendor agenda.

Explore the full schedule at pnsqc.org/conference/2026/at-a-glance.

AI-Powered Testing with Lynqa

Sponsored Content | From our Community Partner: Lynqa

Lynqa is a powerful agentic test execution solution developed by Smartesting. Its specialized AI agent executes manual and Gherkin test cases directly on web applications—without requiring scripts, code, no-code maintenance, or a separate automated test suite.

Lynqa precisely follows test instructions, validates expected results, and generates detailed, step-by-step reports complete with clear execution evidence. By taking over repetitive execution tasks, it helps QA teams accelerate testing cycles, scale execution capacity, and redirect their expertise toward higher-value quality activities.

Natively integrated with Xray in Jira, Lynqa also works seamlessly with XQual, Squash, or as a standalone solution with no external test management tool required.

Speaker Spotlight: Kristine O’Connor

We are thrilled to welcome back Kristine O’Connor, Sr. Agile Transformation Consultant at Agile Rising and PNSQC 2025 Top Presenter Award winner, as an invited speaker for PNSQC 2026!

Session: Signal Before the Storm: How Quality Intelligence Can Stop Harm Before it Becomes Irreversible

When Kristine’s 86-year-old Aunt Cathy was scammed online, every warning sign was present, from a rapid password reset to an unexpected cash advance request. The system failed to recognize that the wrong person was at the controls. In this session, Kristine introduces Quality Intelligence (QI), a framework connecting behavioral, biometric, and transactional signals to prevent harm before it happens.

About Kristine:

With 25+ years in quality, systems thinking, and organizational change, Kristine is a U.S. Navy veteran whose career ranges from hands-on QA to driving enterprise DevOps transformations—saving over $2 million at CVS Health and Aetna. Today, as an SPC, ICP-ACC, and PMI-ACP certified consultant, she supports clients like NASA, Bank of America, Northrop Grumman, IBM, and PetSmart.

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As software continues to redefine physical products, how do we validate systems that are constantly updating over-the-air and operating autonomously?

At PNSQC 2026, automotive quality pioneer Florian Rohde will pull back the curtain in his keynote, Inside Tesla: The Birth of the Software-Defined Vehicle and Quality in the Age of Autonomy. Drawing on his firsthand experience leading Tesla’s vehicle firmware validation team, Florian will explore how the shift toward Software-Defined Vehicles (SDVs) forces us to completely rethink traditional quality assurance.

Instead of treating quality as a final checkpoint, Florian will share how to embed continuous validation directly into the daily development loop. He will unpack the processes, automation tools, and organizational mindset that enable rapid over-the-air (OTA) deployments and utilize real-world data feedback loops to define product quality in the era of autonomy.

Whether you are navigating the complexities of IoT, embedded systems, or autonomous software, you’ll leave with actionable engineering strategies to scale your validation processes and adapt your organization to a rapid-delivery world.

👉 Register for PNSQC 2026 to reserve your spot.

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How to Build a Real-Device Testing Lab That Pays for Itself

Sponsored Content | From our Community Partner: Cambrionix

We are excited to welcome Cambrionix as a Platinum Sponsor of PNSQC 2026!

Cambrionix provides advanced USB solutions for real-device QA environments, helping organizations build reliable and scalable device testing labs. Their technology supports teams at leading technology companies as they test software across real-world devices.

As part of their commitment to the QA community, Cambrionix recently hosted the webinar:

How to Build a Real-Device Testing Lab That Pays for Itself

Now available on demand, this session features Farid Shayegh, who built Snap’s device lab from the ground up. Learn how their team scaled real-device testing to support 12,000+ devices and 700,000 daily test executions, along with practical lessons for building and scaling your own device testing strategy.

Whether you’re creating your first device lab or optimizing an existing environment, this recording offers valuable insights for QA leaders, test engineers, and engineering teams.

Thank you to Cambrionix for supporting the PNSQC community and advancing the future of software quality engineering.

Building the Future Together: A Community Conversation on Innovation

Preparing for the future isn’t the job of any single group—it takes all of us. PNSQC Innovation Day is designed to be a collaborative space where our entire community can gather to learn, engage, and shape what comes next.

Whether you are a student, educator, professional, employer, sponsor, or community leader, we invite you to help us build a stronger tech ecosystem.

How you can get involved today:

• Register & Attend to join the active discussions.

• Volunteer or Sponsor to support our peer-reviewed, all-volunteer community.

Conferences are filled with grand plans for AI. Billion-dollar companies pop into existence on AI bets. Papers and standards are being written on all aspects of AI, including using AI to drive testing and how AI should be tested (e.g. ISO). The future is so bright that I must wear shades, or is it so dark that I want to run and hide?

There are no clear answers or winners yet in AI. I have lived through history where computers appeared in businesses (1960-1970), personal computers became ubiquitous (late 1970s to 1980s), the internet was going to make us all rich (1980s to 1990s), the dot-com bubble (2000), the World Wide Web (WWW is everywhere all the time in the last few years), and now AI. There will always be winners and losers. My interest and this blog posting are about one small slice of AI, testing. This slice will not make you rich. The slice may help make AI-driven cyberism (look that one up) a good time. This post will at least give some references and maybe encourage you to help support the small test slice of the AI mega-pie. These are pointers to starting-point standards for testing, verifying, validating, and evaluating AI.

These AI efforts are done with the support of groups like IEEE and ISO (if you don’t know what those are, use your AI to look them up). There is already an ISO Joint Working Group 2 (designated JWG2) on AI testing. This team is a cross between AI supporters (ISO JTC1/SC 42) and test people (ISO SC7/WG2). You can follow along or even become involved in what is being developed and published by monitoring ISO.org websites or contacting me.

Currently, there are over 20 WG26 ISO/IEC/IEEE 29119 test standards that have either been produced or are in work. The standards provide specifics on the software testing process, documentation, techniques, and specialised areas, including automation, specific product domains, and performance. ISO 29119 Part 1 is freely available from ISO, while other ISO standards must be purchased. Two WG26 standards directly deal with AI. There is a standard’s lifecycle, and there are levels of maturity for these documents that I will not detail here, but again, you can use your AI to look up the details and numbers on specific standards. Some software testing standards are mature and published, while AI standards are in the process of becoming mature enough for release.

On the SC42/JWG2 side, there are 26 or more AI testing standards in progress. Again, maturity is being worked on. You can visit ISO.org and use its search feature to find relevant results.

You can become involved in these efforts through your company if it joins ISO or by going to the “Join IEEE” page. I am the IEEE/US representative for software and AI testing and can answer specific questions (feel free to contact me).

I know a lot of software people, AI supporters, and testers will argue with me and say that standards are complete nonsense, that as an industry, we are not mature enough, and that it’s a waste of time. To be fair, there are aspects of standards that definitely fit that description.

However, for me, standards provide a “line in the shifting sand” from which I can continue my learning and testing journey. Standards are not perfect, so thinking, engineering, and testing will always be needed. But I prefer the structure of standards over what I often see, particularly with some AI systems, which can be rather chaotic (though I do love me some chaos theory, at times).

I provide this blog so readers know about one of the many paths in the AI wilderness, particularly for AI software testing. As Robert Frost observed in his famous poem, as I stand at a crossroads, I will take the path less traveled.

Enjoy thinking, but use AI to help.

About the Author: Jon Duncan Hagar is a semi-retired systems software engineer and testing consultant specializing in software product integrity, verification, and validation, with a focus on embedded and mobile software systems.

With over forty years of experience in software engineering and testing, Jon has supported a diverse range of high-stakes projects, including:

• Control Systems (avionics and automotive)

• Spacecraft & Ground Systems (IT)

• Mobile & Smart Devices

• Attack Testing of new smartphones (with a class and book currently in development)

An active contributor to the engineering community, Jon has delivered over 50 presentations and authored two books spanning software testing, Agile methodologies, product integrity, and quality assurance. He also serves as an IEEE representative for the ISO 29119 software testing standards, the AI ISO JWG2 team, and numerous other IEEE standards committees.

With Great Judgment Comes Great Responsibility

In a world where automation and AI promise to transform software development, how do we make sure we are using technology wisely?

At PNSQC 2026, Paul Grizzaffi will explore the importance of responsible decision-making in software testing and quality engineering in his session, With Great Judgment Comes Great Responsibility.

Drawing lessons from history, technology, and real-world testing practices, Paul challenges the pursuit of “automate at all costs” and reminds us that successful quality engineering is not about using the latest tools—it’s about delivering meaningful value.

Attendees will learn how to:

• Avoid automation efforts that create complexity without value

• Apply technology and AI thoughtfully and responsibly

• Treat automation and AI solutions as software that requires engineering discipline

• Focus on human-centered approaches that improve quality outcomes

As a Principal Automation Architect at FreedomPay and an active contributor to the testing community, Paul brings years of experience helping organizations build practical, sustainable automation strategies.

Join Paul at PNSQC 2026 and discover how good judgment can make the difference between technology that creates value and technology that creates challenges.

ASTQB — PNSQC New Sponsor

Sponsored Content | From our Community Partner: ASTQB

We are pleased to welcome ASTQB as a Sponsor of PNSQC 2026!

As the U.S. board for ISTQB, the world’s leading software testing certification program, ASTQB helps software testing professionals validate their skills, advance their careers, and stay competitive in a rapidly changing industry.

Through certification programs and career resources, ASTQB supports testers and quality professionals throughout their professional journey. Their offerings include the new AI Assurance Pro designation, Soft Skills for Testers certification, and exclusive career benefits available to certified professionals.

Thank you to ASTQB for supporting the PNSQC community and helping software quality professionals grow, learn, and prepare for the future of testing.

Innovation Day: A Community of Learning and Growth

Innovation doesn’t stop with the next generation—it grows through a community where students, educators, researchers, and technology professionals learn from one another.

Innovation Day introduces students to future possibilities in technology, creativity, and problem-solving, while the PNSQC Conference continues that journey by supporting lifelong learning and professional growth for quality professionals.

This connection between students and the broader technology community creates opportunities for sharing knowledge, inspiring innovation, and building the future of software quality together.

Technology professionals, researchers, and educators are invited to get involved as mentors, volunteers, judges, presenters, or demonstration exhibitors during Innovation Day. Share your expertise, inspire the next generation, and help create meaningful connections between emerging talent and the technology community.

Join us in building a community where learning and innovation continue at every stage of the journey.

If you’re a Quality Assurance or technology professional in 2026, you have undoubtedly been impacted by AI-assisted software development.

AI-assisted coding is not necessarily bad—in fact it has lowered the barrier to entry to individuals seeking to be get started in software engineering. Some small projects can be created completely by AI. I’ve not seen large and complex project projects make it that far and there are some reasons why.

The key issue is the non-deterministic nature of AI-produced code. Code and text produced by AI is randomly generated, and the results from Large Language Models (LLMs) vary from one run to another. Sometimes those variances don’t matter, sometimes they do. Consider these two sentences:

The quick red fox jumps over a lazy brown dog.

The quick red fox leaps over the lazy brown dog.

Semantically, those two sentences are the same. A fox propels itself into the air over a dog. However, this sentence is famous because it uses every character in the English alphabet. Changing the word in the sentence from “jumps” to “leaps” means that the second sentence no longer uses the letters ‘j’ or ‘m’. It can no longer be used to test lowercase letters in fonts or to teach typing.

Nuances like the sentence above crop up in ai-generated code all the time and they can be hard to catch if they pass syntax checkers, linters, and compilers. Knowing the importance of that nuance is the job of the QA professional, but if presented with 100s or thousands of them, what are we to do?

Can process help?

AI coding is new, but bugs are not new. Pre-AI, we knew that different people produced software at differing levels of quality, and the industry developed several practices that mitigated these differences. The waterfall software development lifecycle (SDLC) – Requirements, Design, Coding and Testing—served well to convert ideas into deterministic, reliable software, even for mission-critical and life-safety circumstances. The Agile Revolution compressed the waterfall SDLC into smaller, iterative phases, and initially that was a challenge for mission-critical projects, but organizations found that shoring up the requirements and testing phases in these situations was sufficient for creating and shipping solid code.

The SDLC process can’t exactly solve the problem AI-assisted coding introduces because AI-influenced production introduces entropy at all stages of development.

A product manager may create their requirements specifications with AI, programmers and testers may plan and implement their parts of the process with AI. As a consequence, subtle errors are introduced at all stages, and a product may deviate from the original idea in ways not always caught.

The image below shows this from signal processing perspective—the original signal (project intent) is created by AI f(x), but it has some errors e(x), which slightly changes the output. Maybe that matters, maybe it doesn’t.,

One way to stop AI Entropy

I would suggest fighting the problem at its source: The opposite of randomness is consistency. If we cannot rely on AIs to be consistent, we build systems of developing and testing that enforce stable output.

Imagine you have a system under test (SUT) that is being developed very quickly by developers using AI-assistance. If you have requirements, those can be a fundamental rock on which your testing takes a deterministic stance. This means the font package must have valid output for every character. The product must boot without error messages. The report must print on one page. You define a set of test cases for what must happen, no matter what, and this becomes the acceptance criteria. This is, in fact, classic quality assurance.

Scaling Classic QA with tools

The challenge turns out not to be re-inventing QA, but finding and making use of tools to make it possible in a vibe-coded era.

Tool 1: You can use AI to help with test plan creation. LLMs will gladly create test suites, but they often have holes because they don’t understand the product well. This is the subject of several papers at PNSQC this year.

Use your own expertise to define the test plan but take advantage of LLM dictation and review features to save time in writing it up. Anthropic, OpenAI, and Google all have voice-dictation features in their LLM products. If you aren’t working in a crowded environment, you can use dictation to get your ideas to paper faster. Microsoft Word also has a dictation feature if you don’t want to risk having your thoughts go to a chat AI before you can see and correct them.

LLM AIs are generally better at analysis than synthesis. You can take advantage of this by writing a test plan and feeding it and a description of the product under test (or codebase if you have access) and ask the LLM for missed cases and areas of refinement in your test plan.

Tool 2: Use AI to help with tooling and automation. Be careful with this if you’re not already practicing software development, but LLMs can generate software for your test automation suite. An example might be if you have a legacy environment that works with Selenium and WebDriver for web testing: most commercial AIs are trained on those technologies and can update a test set with additional cases. They can also troubleshoot minor issues. You should still “test the tests” that it generates, but the LLM generation saves time.

The key idea is that Software Quality Assurance is essentially the same practice, but we have to take advantage of different tools to ensure quality. Providing feedback on whether something works or not has to be based on evaluative systems or test cases that do not change. Modern LLM-based AI provides the means of creating these test quickly and creating vehicles for running them. Our ability to adapt to these new toolsets will determine how successful we are in the goal of quality assurance.

About the Author

Mitch White is an AI Engineer at Cornell University with over 25 years of experience spanning roles as a Quality Assurance SDET, software developer, manager, and Director of Software Development. He holds an MBA from the University of Denver and a undergraduate degree in Computer Science and Electrical Engineering from MIT. Remotely based in Beaverton, Oregon, Mitch enjoys exploring new technologies and spending time with his family.

As AI systems become increasingly autonomous, how do we ensure they remain trustworthy, reliable, and safe?

At PNSQC 2026, globally recognized AI quality engineering expert Jonathon Wright will tackle that question in his keynote, The AI Assurance Imperative: Quality Engineering for the Agentic Era. Drawing on decades of experience and insights from his upcoming book AI Assurance, Jonathon will explore why traditional quality engineering must evolve to meet the challenges of agentic AI.

Looking for hands-on learning? Jonathon’s workshop, AI Agentic Testing: AI Augmented Testing, will introduce practical approaches to validating AI systems, implementing AI governance frameworks, and using autonomous testing agents to improve quality and reduce risk.

Whether you’re a software tester, developer, quality engineer, or engineering leader, you’ll leave with practical strategies to help your organization build trustworthy AI systems in an AI-driven future.

QA Leadership Summit: Moving AI Testing from Pilot to Production

Sponsored by BrowserStack | PNSQC Community Partner

Most QA teams I talk to have run an AI testing pilot by now. Far fewer have figured out how to actually get from a pilot to something production-grade that leadership trusts.

That gap is exactly what BrowserStack's QA Leadership Summit is taking on this summer. The theme is building an AI-first QA organization, moving past scattered proof-of-concepts toward workflows that hold up on speed, coverage, and defensible quality signals.

Worth a look if you lead a testing or QA org.

Join the summit if you are wrestling with how to scale quality without losing control:

Register here!

The lineup brings together QA leaders and practitioners across keynotes, panels, and hands-on labs. If you are wrestling with how to scale quality without losing control.

Innovation Day: Students Shaping Innovation

Innovation Day isn’t just designed for students, it’s being built with them.

Through the Student Ambassador program, students have the opportunity to help shape the event, welcome attendees, support activities, and inspire their peers while gaining valuable leadership experience. It’s a chance to build new skills, connect with industry professionals, and play an active role in creating an unforgettable STEM experience.

Whether you’re a student looking to get involved, an educator encouraging future leaders, or a community member who wants to support the next generation, we’d love to have you join us.

Interested in making an impact? Learn more about our Student Ambassador and Volunteer programs and help make Innovation Day a success.

Just in the past four months I have survived two rounds of layoffs with no warning from management nor a reason for how the people was chosen to be let go. Of course we were given the generic reasoning of trimming fat to keep the company moving forward. In the days following each lay off, I noticed moral was at an all time low. Everyone had questions we never given answers to and we were all feeling very vulnerable about job security. I know I am replaceable but at what cost to the company? I see other companies doing the same thing of laying off employees and asking those remaining to use AI to increase throughput. Once companies have laid off their lower preforming employees, will they come for those of us who are more senior? I definitely understand the need to move with the times, but I just have to hope upper management knows the value of keeping skilled employees.

While I am fulling embracing AI to work more efficiently, I also understand there needs to be a human element in creating software. I see countless intern openings in software with one of the requirements being “Experience with AI”. To me this is a huge warning sign. I want to hire interns who are learning technical skills at university to apply at a company and then the company can teach the interns on the job how to use certain AI tools. I worry if an intern already has AI experience, they might not have enough technical experience to be successful in the long term.

Personally, I know I am meeting all the benchmarks set out by my company and in some cases exceeding them. This is giving me the knowledge that if I am laid off, it will not be because I am a bad employee. On a daily basis I am reminding myself I have a good job, I am contributing to the company and our customers, and I value my teammates. By reminding myself of these positive aspects, I am able to keep moving forward which I think is the best way to survive these turbulent times. I implore you to focus more on the positive aspects of your daily tasks, even something as small as finding a great bug. The world is a scary place and if we only ever focus on the negative aspects, our ship is going to sink. By focusing on the small victories we get, I think we can bring a positive outlook to work and have a small impact on the work culture.

About the Author: Anna Sharpe is a full-time software test engineer with over a decade of experience in the tech industry, currently focusing on healthcare software from her remote office in Sacramento, CA. A lifelong learner, Anna graduated from Whitworth University with a BS in Computer Science and is passionate about expanding her testing toolbox and mentoring teammates. Outside of her day job, she channels her technical skills into a part-time passion project: building and designing websites for non-profit organizations. Connect with Anna on LinkedIn.

The PNSQC STEM Fair is about more than projects and competitions. It is an opportunity to help students prepare for a future shaped by rapid advances in technology.

Artificial intelligence, robotics, cybersecurity, software, and automation are transforming the way we live and work. Preparing the next generation for these changes requires more than classroom learning. It takes hands-on experiences, opportunities to ask questions, and connections with professionals who are building the future.

By bringing together students, parents, educators, and technology professionals, the PNSQC STEM Fair creates an environment where curiosity is encouraged, ideas are shared, and future careers begin to take shape.

As technology continues to evolve, investing in STEM education and community engagement has never been more important. Together, we can help inspire the next generation of innovators, problem-solvers, and leaders.

Learn more about the PNSQC STEM Fair and how to get involved: PNSQC Innovation Day

Our New Sponsor: Trimble

Sponsored Content | From our Community Partner: Trimble

We’re proud to have Trimble as a sponsor of PNSQC this year.

Trimble is a global technology company connecting the physical and digital worlds — transforming how work gets done across essential industries like construction, geospatial, and transportation. Through precise positioning, modeling, and data analytics, Trimble helps customers build and maintain infrastructure, design and construct buildings, optimize global supply chains, and map the world.

Their support helps make events like PNSQC possible, and we’re grateful for their partnership in advancing the software quality community.

Learn more about Trimble (Nasdaq: TRMB) at www.trimble.com.

Speaker Spotlight: AI Alone Found 5% of Bugs. Maaret Pyhäjärvi Explains Why

If you want to realistically integrate AI into your QA workflow without sacrificing quality, don’t miss Maaret Pyhäjärvi at PNSQC this October.

As the Director of AI-Enhanced Application Testing at CGI, Maaret recently benchmarked testing results on a known-buggy ToDo application to see how human insight compares to GenAI:

• Human + AI: Found 73 issues (100%)

• Humans Alone: Found an average of 18%

• AI Alone: Found a meager 5%

The Takeaway: AI won’t replace testers, but testers who effectively leverage AI will completely outperform those who don’t. True quality requires human exploratory expertise to guide the tool.

Catch Maaret’s Sessions

• The Presentation: Better Together: Humans, AI, and the Next Quality Benchmark

• The Hands-On Tutorial: Social Software Testing Approaches Learn how to leverage pair testing, ensemble programming, and team bug bashes to eliminate engineering “ping-pong.” (Note: Requires 1 laptop between every 2 people).

Register for PNSQC 2026 to See Maaret Live

New PNSQChronicles: Behind the Scenes with Philip Lew

What makes PNSQC different from other software quality conferences?

In this episode of PNSQChronicles, Michael Larsen talks with PNSQC President Philip Lew about the conference from a unique leadership perspective. Philip shares his journey from first-time attendee to president of the volunteer-led nonprofit and discusses what makes PNSQC special—from its peer-reviewed papers and technical depth to the community that has kept the conference thriving for decades.

Whether you’re a longtime attendee or considering PNSQC for the first time, this conversation offers an inside look at what makes the conference a valuable experience for software quality professionals.

Watch the latest episode and learn more about the people behind PNSQC.

What began as a simple high school robotics demonstration at the last PNSQC conference has evolved into something much bigger.

On Sunday, October 11, 2026 (1:00 PM – 5:30 PM), the day before the main PNSQC Conference begins, we will host our first-ever PNSQC Innovation Day at the University Place Hotel in Portland, OR.

Innovation Day is designed to bring our entire community together—students, educators, parents, and technology professionals—to explore the future of learning and technology. This unique event combines hands-on student projects, live robotics, and educational seminars into a shared experience where curiosity meets opportunity.

What to Expect:

• Student Project Displays: Brilliant young minds showcasing research, ideas, and prototypes in STEM, AI, software, and cybersecurity.

• Robotics Demonstrations: Local student teams putting their engineering, testing, and problem-solving skills to the test in live competition activities.

• Educational Seminars: Practical, real-world discussions tailored for parents, teachers, and professionals looking to understand AI and the rapidly changing tech landscape.

Our goal is simple: create opportunities for students to explore future possibilities, connect with industry professionals, and help everyone better understand the technologies shaping our world.

Get Involved & Spread the Word!

Interested in participating? Student project registration is now open! We also welcome robotics teams, volunteers, mentors, and community partners.

• Help Us Share: Please forward this to any students, educators, parents, or community members who might want to be a part of this exciting day.

• Learn More: Check out the official 2026 Innovation Day page for full details.

• Questions? Reach out directly to outreach@pnsqc.com to find out how you can get involved.

Welcome Our New Sponsor: Tricentis!

Sponsored Content | From our Community Partner: Tricentis

We are thrilled to welcome Tricentis as a platinum sponsor of PNSQC 2026!

Tricentis is a global leader in agentic quality engineering. The Tricentis Agentic Quality Engineering Platform leverages the power of AI alongside decades of technology and expertise to provide a fundamentally different way to ensure software quality across complex enterprise application environments.

Why It Matters:

• Intelligently Driven by AI: A totally automated, fully codeless approach to quality.

• Enterprise Scale: Addresses both agile development and complex enterprise applications.

• Proven Impact: Enables organizations to accelerate digital transformation by dramatically increasing software release speed, reducing costs, and improving quality.

Widely credited for reinventing software testing for DevOps, cloud, and enterprise applications, Tricentis is recognized as a leader by major industry analysts including Gartner, Forrester, and IDC. They support more than 3,000 customers globally, including household brands like T-Mobile, Dolby, and Jaguar Land Rover.

We are incredibly grateful for their support in making this year’s conference a success!

Lock In Early Bird Pricing Now!

The Super Early Bird rate has officially flown the coop, but you can still score a sweet deal. Grab your ticket at the Early Bird rate of $1,450 before prices go up again!

• What: PNSQC 2026 Conference & Workshops

• When: October 12–14, 2026

• Where: University Place Hotel, Portland, OR (or streaming virtually!)

• The Perk: Serious career growth + Portland’s legendary food scene.

Whether you’re eyeing a Voodoo Doll donut or mastering automated testing pipelines, your journey to quality starts here.

Secure your Early Bird spot today!

Subscribe now

Let’s talk about a new risk area I recently identified and want to share with you today. At a recent conference, I shared with the QA Engineers that AI is not so much a thinking technology as it is a matching technology. The very measurement of Tokens refers to the smallest possible match. At its core, AI frameworks have captured vast amounts of existing information primarily from the internet. When AI works with word structures such as “Once upon a …,” its algorithms refer to the volumes of content it has consumed and derive the next logical word as statistically likely to be “time”. An interesting reality of how AI pays attention to data and instructions is that it places more emphasis on the beginning and ending of content and less on the middle. I have coined this reality the Horseshoe Paradigm, and within it lies a new type of risk we need to be aware of and test for.

If the application we are testing has AI integrated, results derived from datasets are more likely to be incorrect in the middle regions. Incorporate techniques such as equivalence partitioning, statistical sampling, or anomaly detection to identify these inaccuracies, ensuring your testing strategy accounts for the Horseshoe Paradigm.

Use the following three assessments to determine if you should enhance your testing approach to target defects within the Horseshoe specifically.

1. First, we need to understand the design of the application under test. Is AI being used within the application to derive results? Was AI utilized in the development of that code?

2. Next, we need to understand that the middle of datasets and calculated results are much more likely to be inaccurate if AI is being used to derive those results. Pay particular attention to decimal numbers.

3. Lastly, we should understand the concepts of an “Oracle,” which is a human subject matter expert who provides certified data used in your testing validations. It’s critical not to rely on AI to generate this data due to the Horseshoe Paradigm and the higher risk of invalid calculations in the middle of large datasets.

Utilize these three questions to help you mature your testing approach as you adapt to the new technology benefits and challenges of AI. Realize that there are very few “experts” in the field of AI at this point. As we uncover new risks, such as the Horseshoe Paradigm, you will need to adapt your approach to account for them.

As AI becomes embedded in more of what we test and how we build, our job as Quality Engineers isn't shrinking — it's expanding into territory that didn't exist five years ago. The Horseshoe Paradigm is one piece of that map. For another, read my piece on Silent Confidence.

About the Author

Greg Paskal is a testing innovator with over 40 years of experience, known for pioneering sustainable and maintainable approaches to both manual and automated testing. He is the creator of METS (Minimal Essential Test Strategy) and the author of Test Automation in the Real World, available on Amazon. A dedicated mentor and lifelong learner, Greg shares his insights through writing and his YouTube channel, Craft of Testing.

Learn more at CraftOfTesting.com, METSTesting.com, and GregPaskal.com, or connect with him on LinkedIn.

The early bird catches a worm, but the Super Early Bird catches all the bugs! Lock in the absolute lowest rates for PNSQC 2026 before they fly away.

Secure your spot now: pnsqc.org/register

Supporting Quality Innovation: PractiTest Joins PNSQC 2026 as a Gold Sponsor

^{Sponsored Content | From our Community Partner: PractiTest}

We’re excited to welcome PractiTest as a new Gold Sponsor of PNSQC 2026.

PractiTest is an AI-powered Test Management and QA Intelligence platform that goes beyond test execution to deliver meaningful business insights. While tests run, defects are logged, and dashboards fill up, the most important questions often remain unanswered: Are we ready to release? Are we covered where it matters most? What is our actual level of risk?

PractiTest closes that gap by transforming QA data into clear indicators, actionable insights, and recommended next steps. With real-time visibility into risk, coverage, and release readiness, stakeholders can make informed decisions with confidence. Built on a flexible data model, seamless integrations, SmartFox AI, and powerful QA Intelligence capabilities, PractiTest helps teams always know where they stand and release with confidence.

Learn more at PractiTest.com or connect with the team on LinkedIn.

New PNSQChronicles: The Signal Before the Storm with Kristine O'Connor

What if the warning signs of harm are already visible but we’re not looking for them?

In the latest episode of PNSQChronicles, Kristine O’Connor, Senior Agile Transformation Consultant at Agile Rising and a PNSQC Top Presenter Award winner, joins Michael Larsen to discuss Quality Intelligence and how quality professionals can help identify risks before they become irreversible.

Inspired by her aunt’s experience as the victim of a financial scam, Kristine explores how telemetry, behavioral data, and a deeper understanding of customer needs can help organizations detect emerging threats, prevent harm, and improve outcomes.

This thought-provoking conversation examines the intersection of software quality, customer experience, empathy, risk prevention, and challenges us to think beyond defects toward the real-world impact of our systems.

Subscribe now

In software development, finding a bug late costs exponentially more to fix. The same logic applies to your professional development! Why delay what you can optimize right now?

It’s time to practice what we preach: Shift Left Your Registration. By securing your spot early, you don’t just stay ahead of the curve—you also unlock the absolute best rates. Our Super Early Bird discount is live, but it won’t last forever.

Don’t let this opportunity timeout. Head over to our registration portal and lock in your pass today:

Register Now for PNSQC 2026

Let’s build, test, and connect—starting early!

Welcoming Gold Sponsor Xray + OMG Tech Partners

Sponsored Content | From our Community Partner: Xray + OMG Tech Partners

PNSQC is pleased to recognize Xray + OMG Tech Partners as a Gold Sponsor of PNSQC 2026.

Xray completes the DevOps workflow, enabling teams to test smarter, release faster, and deliver high-quality software at scale. As a leading test management solution for Jira, Xray helps organizations improve visibility, traceability, and quality throughout the software development lifecycle.

OMG Tech Partners, an Atlassian Gold Solution Partner, helps organizations in highly regulated industries transform complexity into clarity through compliance-ready solutions. Their expertise supports teams navigating governance, risk, and regulatory requirements while maximizing the value of the Atlassian ecosystem.

Together, Xray and OMG Tech Partners help organizations make sense of complex environments, modernize the way they operate, and implement Atlassian solutions that stand up to the highest levels of scrutiny.

Learn more about Xray at https://www.getxray.app/

and follow them on LinkedIn

Thank you to Xray + OMG Tech Partners for supporting PNSQC and the software quality community.

New Community Voices Series Launches

We’re excited to launch a new series of articles featuring thought-provoking perspectives from leaders across the quality community. And yes, you can become a guest author too!

Our first article is:

Are We Really Quality Professionals?
Why a 25-year career shift made one industry veteran rethink a title that millions of software testers take for granted.

In this article, Jan Jaap Cannegieter, an IT Advisor for the Dutch Government with more than 30 years of experience in software testing, quality assurance, and business analysis, challenges us to look beyond job titles and reconsider what it truly means to be a quality professional in today’s rapidly evolving technology landscape.

Read the article and join the conversation:

Interested in sharing your own experiences, lessons learned, or ideas with the quality community? PNSQC welcomes guest authors from across the software quality ecosystem.

Subscribe now

After roughly 25 years of working in software testing on a regular basis, I no longer work in testing at all. Today, I work for the Dutch government, where I assess large-scale projects to determine whether they are being managed effectively and advise them on how to improve their governance and control. In these reviews and advisory engagements, testing is only occasionally a topic of discussion.

When you step back from testing, as I have, you start noticing things that previously seemed perfectly normal. One of those things is the use of job titles by testers that include the word quality: QA Specialist, Quality Officer, Quality Engineer, and similar variations. For the sake of simplicity, I will group all of these titles under the umbrella term quality professional.

The reasoning behind these titles is familiar to most testers, and it was to me as well until recently. The argument goes something like this: by identifying defects and issues, testers help improve product quality, provided those issues are addressed. Therefore, we contribute directly to quality. In the minds of many testers, that makes us quality professionals.

There is no question that a system’s quality improves when the defects we identify are resolved. But does that automatically make us quality professionals?

From the perspective of my current role, the answer is no.

The concept of quality in my current environment is far broader than product quality alone.

Quality Assurance Beyond Testing

In my current context, every major project has a Quality Assurance Officer. This person organizes a wide range of activities aimed at assessing and, where necessary, improving quality. These activities include peer reviews, formal assessments, delta reviews, milestone reviews, and other independent evaluations.

The focus of these reviews can vary considerably. They may examine governance structures, project progress, stakeholder engagement, business feasibility, technical viability, or other factors that influence a project’s likelihood of success.

Interestingly, testing is rarely a primary focus of these assessments.

Occasionally, I interview someone involved in testing. This isn’t because testers have a special role in quality assurance activities, but because they often possess valuable insights into how a project is actually performing. They see problems, risks, and inconsistencies that others may overlook.

However, from a roles-and-responsibilities perspective, testers do not occupy a privileged position within these quality assurance activities. The primary objective of these reviews is project quality: determining whether a project is likely to achieve its goals successfully.

Quality at the Organizational Level

In my current environment, we also have Quality Officers. Their responsibility is quality at the organizational level, with a strong focus on processes rather than products.

Examples include:

• Program and project management processes

• Lifecycle management processes

• Security processes

• Privacy processes

• Data management processes

In practice, these Quality Officers almost never deal with testing. Their focus is ensuring that organizational processes are defined, followed, measured, and continuously improved.

Three Different Types of Quality

This is why the claim that testers are quality professionals does not fit well within my current context.

Here, testers are simply testers. Their responsibility is to independently determine whether a system behaves as intended. That is an important responsibility, but it is also a specific and limited one. The responsibilities of the quality professionals around them are significantly broader.

You could argue that there are at least three distinct types of quality:

1. Product Quality: Whether the system itself meets expectations and requirements.

2. Project Quality: Whether the project is being managed in a way that maximizes the likelihood of success.

3. Process Quality: Whether the organization’s processes are effective, controlled, and continuously improving.

Testers certainly influence product quality, although they are by no means the only professionals who do so. Developers, architects, product owners, business analysts, and many others contribute as well.

When it comes to project quality and process quality, however, testers in my current context have little or no direct responsibility.

A Claim Worth Questioning

Perhaps that means we should be more careful when we describe ourselves as quality professionals.

If quality encompasses product quality, project quality, and process quality, then most testers operate primarily within only one of those domains. While our work can contribute significantly to better products, that contribution alone may not justify claiming ownership of quality in its broadest sense.

At least in my current environment, that claim is difficult to defend.

Testers are not quality professionals because they improve quality. They are testers because they provide independent information about quality, and there is a meaningful difference between the two.

Jan Jaap Cannegieter is an IT Advisor for the Dutch Government with over 30 years of expertise in software testing, quality assurance, and business analysis. A recognized international expert, he has twice featured as a speaker at the prestigious Pacific Northwest Software Quality Conference (PNSQC).

Spend $320 on Portland, Not Registration Fees!

Portland is famous for its creativity, independence, and willingness to try new ideas. That same spirit is what makes PNSQC special.

At PNSQC, you’ll find practitioners sharing real-world lessons, challenging conventional thinking, and exploring what’s next in quality, testing, AI, and software engineering. It’s a conference built by the community, for the community, and it embraces the curiosity and innovation that make Portland unique.

Whether you’re looking for practical takeaways, fresh perspectives, or meaningful conversations with fellow professionals, PNSQC is the place to be this fall.

Early registration is now open, and the Super Early Bird rate is the best way to secure your spot while saving money.

Join us in Portland. Keep learning. Keep questioning. Keep Portland weird.

Register early and save: PNSQC Registration

PNSQChronicles: Discussing the "AI Assurance Imperative" with Jonathon Wright

As AI systems become increasingly autonomous, traditional approaches to quality assurance are being pushed beyond their limits. In this episode of PNSQChronicles, Michael Larsen sits down with keynote speaker Jonathon Wright to explore what quality engineering looks like in the age of agentic AI.

Jonathon discusses:

• the evolution of AI Assurance

• the emergence of Confidence Engineering

• why testing AI-powered systems requires a fundamentally different mindset than testing traditional deterministic software.

From non-deterministic behavior and autonomous agents to governance, ethics, and the future role of software testers, this conversation examines how the testing profession must adapt to meet the challenges of an AI-driven world.

If you’re curious about the future of quality engineering, AI governance, and what comes next for the testing profession, this is a conversation you won’t want to miss.

You can hear more from Jonathon in his Keynote Address, “The AI Assurance Imperative: Quality Engineering for the Agentic Era” at PNSQC 2026.

Share Your Ideas. Save 50%

Have an idea, case study, research project, lesson learned, or innovative approach to software quality?

Submit a poster proposal for PNSQC 2026.

Poster sessions are one of the best ways to start conversations, share practical insights, and connect with fellow quality professionals in a relaxed, interactive setting.

Accepted poster authors receive 50% off conference registration, making it an excellent opportunity to share your work while attending the conference at a significant discount.

Space is limited.
Poster proposals will be accepted through September 22.

Whether you’re a first-time presenter or a seasoned conference speaker, a poster is a great way to contribute to the community and showcase your ideas.

What could you teach the quality community this year?

Learn more and submit your proposal: https://pnsqc.org/conference/2026/poster/

PNSQC June Meetup: Tech Talk Team Up with Portland AWS User Group

PNSQC is teaming up with the Portland AWS User Group for our June Meetup at ProFocus Headquarters!

Join us for an evening of networking and technical learning with cloud, security, and quality professionals from across the region.

This month’s talk:
Trevor Gudeman, Senior Software Engineer at Pacific Northwest National Laboratory, will explore how AWS-native tools like CodeBuild and CodePipeline can improve code quality across CI/CD workflows through automated linting, testing, security scanning, quality gates, and more.

📅 Monday, June 15

🕠 5:30 PM – 7:30 PM PDT

📍 Hybrid event: attend in Portland or online

RSVP through Meetup and join us!

We’re almost done with our review of the abstracts submitted for PNSQC 2026. During the evaluation process, I got about a third of the way through the pile and realized I had stopped skimming (We had over 100 abstracts). I was actually reading. Many talks were asking and answering harder questions than I’ve heard at any conference in a while.

The conversation about agentic AI is getting serious

The most crowded subject in the submissions was agentic testing. Not the hype version, where someone declares that agents will replace testers and then does a demo. The practitioners coming to Portland are bringing real battles and experiences.

One paper makes a case: We’ve spent years learning to test deterministic systems, and agentic AI breaks that contract entirely. Same inputs, different paths. Test suites that pass don’t tell you the agent stayed inside its intended boundaries. A whole session is dedicated to something the author calls the “tautology trap,” a failure mode where tests look behavioral but never actually call the agent. 14,000 passing tests. Zero behavioral validation. Green pipeline the whole time. I was thinking this may start some uncomfortable conversations in the hallways.

Several other papers work the same territory from different angles. One digs into why AI coding assistants have created a validation bottleneck nobody fully anticipated. Developers ship faster; everything downstream gets slower. Another builds a governance framework for autonomous test automation from the inside out: impact analysis, quality scoring, and confidence reporting replacing the raw pass/fail percentage that doesn’t actually tell a stakeholder what they need to know.

What strikes me reading all of these together is that the field is past the “should we use AI” question. The question is now: how do we use it without fooling ourselves?

The human question isn’t going away

Some of the papers I’m most excited about aren’t primarily about tools at all.

One of them asks whether we’ve been optimizing away something we can’t replace. Not human effort, but human judgment. The kind of quality that comes from someone who cares about what ships, who understands the person on the other end, who will flag something even when the test passed. Is caring something that can be duplicated by a machine? There’s a phrase in the abstract I keep thinking about: “quality is art.” Unprovable, probably. Worth arguing about.

Another paper approaches this from a different direction: what happens to team health when the systems we build become more autonomous but the humans running them stay just as human? It draws on real research from organizational psychology as the actual argument.

And then there’s a paper from a program manager who spent his MBA dissertation interviewing 20 C-suite and VP-level leaders about why communication fails during sustained change. By the time the message reaches the people who have to act, the context that makes it matter has been stripped out. He calls it “communication debt.” I’ve seen this in many clients I’ve worked with. It has nothing to do with AI and everything to do with why good initiatives die in middle management.

Some of the most interesting work is coming from unexpected places

A team of high school students from Portland submitted a paper on autonomous robotics software architecture. Not a science fair project. A real engineering paper about the quality failure modes that emerge when you move from finite state machines to belief-based decision models. They’ve been building competitive robots for years and they documented what breaks, why it breaks, and what they changed. I’ve read papers from senior engineers with less intellectual honesty and rigor than this one.

Here’s something most teams building LLM-powered products don’t know yet. The metric everyone uses to evaluate RAG (Retrieval-Augmented Generation) systems, “faithfulness,” measures whether the model’s answer was consistent with what it retrieved. That’s it. It says nothing about whether what it retrieved was actually true. A financial services architect noticed this gap, built an adversarial test set to measure it across the major evaluation frameworks, and the numbers are not reassuring. You can score 0.95 on faithfulness and be confidently wrong. He’s bringing the data to Portland.

One paper will resonate with anyone who has ever tried to explain to a non-technical executive why a small change cost six months and $400,000. It’s about assessing software maintainability without reading a codebase line by line. How do you communicate technical debt to someone who asks why the developers can’t just fix it over a weekend?

What this year’s program is really about

If I had to name the common thread across all of this work, I’d call it accountability under uncertainty. How do you stay responsible for quality when the systems you’re building make their own decisions? When the tests pass but you’re not sure what they proved? When communication breaks down not because anyone lied but because the message changed on its way?

The community has been digging into these questions. The papers this year aren’t answers. They’re the best current thinking from people who are doing the work, getting it wrong, adjusting, and writing it down for the rest of us.

October in Portland. I’m looking forward to the arguments.

What’s the hardest question your team is dealing with right now?

Want to attend PNSQC but need approval? Focus on the value you’ll bring back to your organization:

Practitioner-Led Content
PNSQC presentations are selected through peer review and delivered by practitioners sharing real-world experiences, not sales pitches.

Focus on Software Quality
Unlike broad technology conferences, PNSQC is dedicated to software quality, testing, quality engineering, and quality leadership.

AI Through a Quality Lens
Learn how organizations are applying AI to testing and quality engineering while addressing reliability, risk, governance, and trust.

Direct Access to Experts
PNSQC’s size makes it easy to engage with speakers, authors, and fellow practitioners, leading to deeper conversations and practical takeaways.

Knowledge You Can Apply Immediately
The conference emphasizes case studies, lessons learned, and proven practices that teams can bring back and implement right away.

Register during the Super Early Bird period to secure the lowest conference rates and get more value from your training budget.

Invest in the Next Generation of Innovators

Support the next generation of innovators, problem-solvers, and technology leaders.

The PNSQC STEM Fair gives students an opportunity to showcase STEM projects, receive feedback from industry professionals, and explore real-world applications of technology. Your sponsorship helps inspire curiosity, critical thinking, and future careers in STEM.

Why Sponsor?

• Connect with future STEM talent

• Support STEM education and workforce development

• Demonstrate your commitment to innovation and community engagement

• Gain visibility among students, educators, and technology professionals

Interested in sponsoring the PNSQC STEM Fair? Contact outreach@pnsqc.org for more information.

PNSQC 2026 is accepting poster paper proposals

No formal paper required. Present your work, your experiment, or your question directly to software quality professionals at one of the Pacific Northwest’s longest-running tech conferences (44 years and counting!)

PNSQC is one of the few practitioner-focused software quality conferences in the world that offers a Poster Paper Program—a rare opportunity to present exploratory research, innovative ideas, or work in progress in a professional conference setting.

THE EXPLORER

You have an idea worth sharing, but not enough data for a full paper—yet. Use a poster to start the conversation and gather feedback.

THE PRACTITIONER

You tried something interesting at work. Maybe it succeeded, maybe it failed. Either way, there’s a lesson others can learn from.

THE EMERGING SPEAKER

You want presentation experience before stepping onto a stage in front of 150+ attendees. A poster session is a great place to start.

THE STUDENT

Your capstone project, thesis, course research, or independent study deserves an audience beyond the classroom.

If any of these sound like you, the PNSQC Poster Paper Program may be the perfect fit.

What You Get

• 50% off conference registration — accepted poster authors pay half price

• A reviewer assigned to give feedback on your poster before the conference

• Direct access to 300 software quality professionals, engineers, and hiring managers

• Publication credit in the PNSQC 2026 program and proceedings

• A real conference presentation credit for your resume or portfolio

• As an added bonus, accepted poster presenters receive a $250 stipend, making PNSQC one of the only conferences to provide financial support for poster authors.

DEADLINE: SEPTEMBER 22, 2026

Submit at pnsqc.org/conference/2026/poster/

Questions? program@pnsqc.org

PNSQC June Meetup: Tech Talk Team Up with Portland AWS User Group

PNSQC is teaming up with the Portland AWS User Group for our June Meetup at ProFocus Headquarters!

Join us for an evening of networking and technical learning with cloud, security, and quality professionals from across the region.

🎤 This month’s talk:

Trevor Gudeman, Senior Software Engineer at Pacific Northwest National Laboratory, will explore how AWS-native tools like CodeBuild and CodePipeline can improve code quality across CI/CD workflows through automated linting, testing, security scanning, quality gates, and more.

📅 Monday, June 15
🕠 5:30 PM – 7:30 PM PDT
📍 Hybrid event: attend in Portland or online
RSVP through Meetup and join us!