Test-Talks.com - The Quality Engineering Podcast

Automation Cyborg

”Saving the world from bad software” - a groundbreaking podcast series that delves into the rapidly evolving world of software testing. Join us as we navigate the cutting-edge technologies, methodologies, and practices that are shaping the future of the testing landscape. In this captivating series, we’ll be featuring thought leaders, industry experts, and practitioners who are at the forefront of innovation in software testing. Our engaging conversations will cover topics such as: 1️⃣ Artificial Intelligence and Machine Learning: Discover how AI and ML are revolutionizing test automation, analysis, and generation, providing testers with powerful new tools and capabilities. 2️⃣ The Human-Machine Collaboration: Uncover the ways in which testers can leverage AI technologies like Keysight Eggplant Test to enhance their skills, boost productivity, and ensure the highest quality software. 3️⃣ Testing in the Age of Agile and DevOps: Learn how modern development approaches have transformed the role of testers and how they can adapt to remain essential in fast-paced environments. 4️⃣ Emerging Testing Strategies: Dive into the latest methodologies, such as context-aware testing and test-driven development, and explore their potential to improve test coverage and effectiveness. 5️⃣ The Growing Importance of Domain Knowledge: Discuss the significance of integrating domain expertise into the testing process and how AI can help testers gain valuable insights into specific industries. 6️⃣ The Ethical and Social Implications of AI-Driven Testing: Examine the ethical concerns and societal impact of AI in software testing, and envision a responsible and inclusive future for the industry. Don’t miss out on this thrilling journey into the future of software testing! Subscribe now to ”Test-Talks.com” and stay ahead of the curve as we redefine the boundaries of quality assurance. #TestingTomorrowToday #SoftwareTesting #FutureOfTesting #Podcast

  1. hace 3 días

    Nordic Testing Days (NTD) - Keynote - The Irreplaceable 40%

    Artificial Intelligence is increasingly used to write test code. Today it is estimated that 60% of new test code is written by AI tools and this number will only continue to rise. Surprisingly, however, the testing is now the critical component needed is to ‘test’ the 40% that AI is unable to test itself, that is where the future of testers. That 40% is not busy work. This is not your typical motivational speech dressed up as another AI testing keynote. What I will talk about in the keynote is the three critical capabilities that are becoming more valuable not less, and the underlying reason for this is that all of them depend on something that current models lack: the human intent behind engineering quality software for other humans to use. Engineer the Context Understanding: While AI can greatly amplify the effectiveness of testing, the quality of results is only as good as the input you give it. In 2026, the testers who will rise to the top will not be the ones who came up with the cutest prompt for the AI to answer. Rather, they will be those who can successfully engineer the context of the problem for the AI to solve. We will look at real-world best practices for testing with AI, and I will share an AI Assurance playbook for context engineering that will immediately raise the quality of your AI-infused testing. Review with Heuristic Judgment: AI generated test suites look good - until they fail to test what really matters silently. Green pipelines are the most dangerous artefact in organisations today. No one asks: what are we not testing? This capability helps organisations audit test suites generated by AI with a sense of heuristic judgement. I can explain how to design world class AI augmented tests like an experienced navigator reading a map to identify the blank spaces where organisation specific risks reside. Orchestrate Trust: Humans trust humans they still need decide when the machine is wrong. That is not a technical skill - it is a quality leadership act. This capability describes the shifting role of quality engineers to trust AI Assurance oracles. This capability explores the new role of orchestrating AI Assurance that trust across teams, tools, and stakeholders, and answers the question: “Who is really responsible for quality?” By the end of this keynote, you will have an assessment of your skills in planning, architecting, designing and leading AI governance, a practical plan of action to implement in the real world, and the assurance that you will be “ready” to become part of the “Irreplaceable 40%”. Key Takeaways: Context Engineering is our New Superpower: AI-augmented tests are only as valuable as the context a human provides. Attendees will leave with a repeatable framework for curating system, user, and business context that transforms AI output from "technically correct" into "intent driven tests" - a skill that compounds in value as the important of testing of AI infused systems improve. Heuristic Auditing Catches the Real Truth: A passing automated test regression suite is not proof of quality; it is proof that the tests you wrote passed. Attendees will gain practical heuristic-led auditing patterns to interrogate value-driven testing, identify dangerous blind spots, and ask the questions that thinking machines are structurally incapable of asking themselves. Trust Orchestration is the Quality Engineer's Next Career: The highest-value skill in an AI-augmented team is not technical testing - it is the ability to calibrate, communicate, and own confidence decisions across people, tools, and stakeholders. Attendees will understand how to position themselves as the trust architect oracles in their organisation increasingly needs, turning a perceived threat into their most durable career advantage.

    50 min

  2. 21 may

    The Confidence Asymmetry Principle - Why 80% of future AI compute will be AI Verification (The Correlated Blindness Penalty Theory)

    The Confidence Asymmetry Principle, AI Verification Economics, Correlated Blindness & When Code Generation Becomes Close to Zero - Confidence Becomes the Quality of the Product! For seventy years the scarce resource of software engineering was the production of correct code, and every methodology of the field was an attempt to manage that scarcity. Generative AI is dissolving it. The marginal cost of producing a plausible candidate program is collapsing toward a floor set only by the price of compute. AI is making software engineering radically cheaper to generate — but not necessarily cheaper to trust. In this episode, we explore The Confidence Asymmetry Principle, a working paper around Confidence Engineering Manifesto by Jason Arbon (2026), which argues that the real economic shift in AI-infused software is not the collapse in the cost of code, but the rising importance of the cost of confidence. As generative AI drives the marginal cost of producing software toward zero, a new constraint emerges: verifying that the generated artifact is correct, safe, reliable, and fit for production. The paper argues that generation and verification are governed by different cost laws — and those laws are now diverging. We unpack the core idea that in an AI software economy, code becomes cheap, but confidence becomes scarce. The conversation explores: Why verification becomes more expensive as generation becomes cheaper The three irreducible floors beneath verification cost: undecidability, execution, and interaction How Rice’s theorem and the halting problem shape the future economics of software testing Why “AI testing AI” can create correlated blind spots rather than genuine assurance The Correlated-Blindness Penalty and why independent evidence matters The Confidence Bound and the cost of reaching justified confidence Why verification compute may become the dominant workload of future AI compute How Confidence Engineering and AI Assurance emerge as practical responses to this shift The Confidence Asymmetry Principle the work of Jonathon Wright and Jason Arbon, in this episode reframes the AI engineering productivity revolution through a sharper lens: the bottleneck is no longer whether AI can generate software, but whether organizations can produce enough evidence to trust what it generates. The constructive consequence is not pessimism. It is that verification becomes the central discipline of software engineering, and that the discipline already has a name. Confidence Engineering — and its companion, AI Assurance — is the practice the Principle demands: justified confidence over performative coverage, decorrelated evidence over correlated convenience, continuous evaluation over static validation, a probability with an interval over a green check The next era of software will not be defined by who can generate the most code. By The Confidence Asymmetry Principle, generation is becoming free, and free things do not confer advantage. The era will be defined by who can justify confidence in what has been generated — at a known residual risk, on evidence whose independence can be exhibited and audited. When code becomes free, confidence becomes the product. The work, and the compute, and the value, follow the confidence, #TestTalks #ConfidenceEngineering #AIAssurance #AgenticAI #AITesting #QualityEngineering #TrustworthyAI #SoftwareTesting #LLM #AIConfidence #GenAI #AIVerification

    2 h y 7 min

  3. 21 may

    World Digital Report 2026 - The Future of Quality Engineering - AI Confidence Engineering, AI Literacy & AI Assurance

    AI adoption is accelerating at a pace few organizations are prepared for. In this episode, we dive deep into the findings of the World Digital Report 2026 — one of the most comprehensive global studies examining the intersection of AI, digital transformation, quality engineering, enterprise trust, and the future of work. Featuring insights and research from Jonathon Wright alongside contributors including Jason Arbon, Joel Deutscher, Alex Belotsky, Ben Daniels, Ethan Bhundia,  Milarna Parkerpayne, & Paul Gerrard this conversation explores what happens when organizations deploy AI faster than they develop AI literacy, governance, and assurance capabilities. We unpack some of the report’s most important themes: Why AI literacy has become the defining enterprise capability of 2026 The rise of Agentic AI and autonomous systems inside modern organizations How quality engineering roles are evolving from execution toward AI Confidence Engineering The widening gap between AI adoption and organizational trust Why prompt engineering, AI governance, and AI assurance are now business-critical The challenge of testing non-deterministic systems at enterprise scale How digital transformation is shifting from automation toward autonomous orchestration We also explore the broader implications of AI-powered productivity, enterprise confidence, software quality risk, and the future of human-machine collaboration as organizations move from experimentation into operational dependency on AI systems. From AI-generated testing and autonomous workflows to assurance engineering and organizational readiness, this episode examines the data behind one of the largest digital transformation shifts seen in decades. Because the question is no longer whether organizations will adopt AI. The real question is whether they can do it safely, responsibly, and with confidence. #TestTalks #WorldDigitalReport #AI #AgenticAI #AIAssurance #QualityEngineering #DigitalTransformation #AITesting #TrustworthyAI #GenAI #FutureOfWork

    2 h y 31 min

  4. 8 abr

    AI Engineering Assurance - The Irreplaceable 40% - The future of software is Open-Testing.ai!

    Artificial Intelligence is increasingly used to write requirements. Today it is estimated that 60% of new requirements artifacts are generated by AI agents — user stories, acceptance criteria, specifications, even test scenarios — and this number will only continue to rise. Surprisingly, however, the critical capability now needed is to validate the 40% that AI is unable to assure itself. That is where the future of requirements engineers, business analysts, and quality professionals lives. That 40% is not busy work. This is not your typical motivational speech dressed up as another AI requirements keynote. What I will talk about in this keynote are three critical capabilities that are becoming more valuable, not less, and the underlying reason for this is that all of them depend on something current models lack: the human intent behind engineering quality requirements for other humans to build, test, and trust. Engineer the Context for Requirements: While AI agents can rapidly generate requirements artifacts at scale, the quality of those requirements is only as good as the context you give the AI to work with. In 2026, the professionals who will rise to the top will not be the ones who came up with the cutest prompt for an AI agent to draft a specification. Rather, they will be those who can successfully engineer the context of the business problem — stakeholder intent, domain constraints, regulatory obligations, and outcome expectations — so that AI produces requirements worth building from. We will look at real-world best practices using OpenRequirements.AI and its DeFOSPAM methodology (Definitions, Features, Outcomes, Scenarios, Prediction, Ambiguity, and Missing data), and I will share an AI Assurance playbook for context engineering that will immediately raise the quality of your AI-generated requirements. OpenRequirements.AI deploys seven specialized analyst agents that interrogate AI-generated requirements the way an experienced business analyst would — finding the gaps, ambiguities, and unstated assumptions that AI agents structurally cannot identify in their own output. Review with Heuristic Judgment: AI-generated requirements look comprehensive — until they silently miss what really matters. A complete-looking specification is the most dangerous artifact in organizations today. No one asks: what requirements are we not capturing? What scenarios have we not considered? This capability helps organizations audit requirements generated by AI with heuristic judgment. Using OpenTest.AI and its 33+ specialized virtual tester profiles, I will explain how to stress-test AI-generated requirements the way an experienced navigator reads a map — identifying the blank spaces where organization-specific risks, edge cases, and unstated business rules reside. OpenTest.AI doesn't wait for code to exist; it tests the requirements themselves, surfacing defects at the point where they are cheapest to fix and most expensive to ignore. Orchestrate Trust in AI-Generated Specifications: Humans trust humans. Stakeholders, developers, and regulators still need to decide when the machine-generated requirement is wrong, incomplete, or dangerously plausible. That is not a technical skill — it is a quality leadership act. This capability describes the shifting role of requirements professionals and quality engineers into AI Assurance oracles. It explores the new responsibility of orchestrating trust across AI agents, validation tools, development teams, and business stakeholders, and answers the question: "Who is really responsible for the quality of requirements that no human originally wrote?" By the end of this lesson, you will have an assessment of your skills in planning, architecting, designing, and leading AI requirements governance, a practical plan of action using OpenRequirements.AI and OpenTest.AI that you can implement immediately, and the assurance that you will be "ready" to become part of the "Irreplaceable 40%."

    51 min

  5. 20 mar

    Vibe Requirements Engineering - 10 days effort in 10mins for 10p value $10k with OpenRequirements.AI

    Explore the critical role of an "requirements engineering in software engineering," detailing how a robust "vibe engineering requirement specification in ai native engineering" underpins successful "vibe engineering development." This video provides a direct look at the documentation essential for "vibe requirements engineering," showcasing how such specifications guide the creation of precise and reliable software. 🚀 $10k of value in less than 10mins? (based on human effort required to generate the same or less value)

    21 min

  6. 20 feb

    From AI Experiments to Real Industry | World Agentic AI Summit

    Catch the latest on the future of AI at the World Agentic AI Summit. This presentation explores the shift from experimentation to industrialization, focusing on current ai trends and the impact of artificial intelligence on business and technology. Discover key takeaways for scaling ai in various sectors. 🚀

    23 min

  7. 27 ene

    Agentic AI - Model Learning Based Testing (MlBT) - Digital Twin Testing (DTDL) meets Context Understanding (RARE / MIRAGE)

    Test-Talks.com welcomes Karl McCarron an ML Architect at Keysight within the Eggplant AI R&D labs team in Cambridge, where he defines technical solutions and researches emerging AI technologies to advance the product's future vision in AI Agentic Testing. With a strong foundation in theoretical physics, having completed his MASt in Applied Mathematics from the University of Cambridge with Distinction and a First Class Master's in Physics from the University of Oxford, his work bridges advanced mathematical concepts with practical machine learning applications. His expertise spans from quantum field theory and particle physics to applied ML architecture, bringing rigorous analytical approaches to enterprise-grade intelligent automation. Join the Eggplant AI R&D Labs team as they boldly reimagine the future of software testing in this thought-provoking brainstorming session. Exploring everything from transforming legacy test assets into intelligent models to the emergence of "model learning testing," they tackle the hard question: how do we make testing more valuable in an AI-driven world? Dive into discussions about context engineering, agentic AI systems, digital twins, and why 90% of software testing might need a complete rethink. If you're curious about where testing is headed in 2026 and beyond complete with newly coined concepts like "software archeology" and "value-informed testing" this wide-ranging conversation is essential listening for anyone in QA, test automation, or software development.

    1 h 11 min

  8. 30/12/2025

    AI-Assurance - Vibe Quality Testing of AI Infused Systems (feat. SureLine.AI)

    Test-Talks.com welcomes Adam Sandman who has been working in the IT industry for the past 25 years in areas such as architecture, agile development, testing and project management. Currently Adam is the Founder and CEO of Inflectra Corporation, where he is interested in technology, business and enabling people to follow their passions. At Inflectra, Adam has been responsible for researching the tools, technologies and processes in the software testing and quality assurance space.  In this episode will be a world exclusive of SureLine.AI Go From Not Worrying About Your AI Agents to Knowing They are Safe, Reliable, and Effective: Ask SureLine™ to test your agent, like you were talking to an experienced test manager on your team. SureLine™ will design and implement dynamic tests, using collections of bespoke testing agents. It provides a report of the AI under test, highlighting strengths and weaknesses, risks, quality concerns, and more. SureLine™ is a whole new category of QA tool. Testing AI is not like normal testing. You can’t rely on traditional testing to really know the state of your AI. You need a quality assurance tool built from the ground up, that leverages AI to dynamically probe and test AI. You need SureLine™.

    1 h 10 min
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”Saving the world from bad software” - a groundbreaking podcast series that delves into the rapidly evolving world of software testing. Join us as we navigate the cutting-edge technologies, methodologies, and practices that are shaping the future of the testing landscape. In this captivating series, we’ll be featuring thought leaders, industry experts, and practitioners who are at the forefront of innovation in software testing. Our engaging conversations will cover topics such as: 1️⃣ Artificial Intelligence and Machine Learning: Discover how AI and ML are revolutionizing test automation, analysis, and generation, providing testers with powerful new tools and capabilities. 2️⃣ The Human-Machine Collaboration: Uncover the ways in which testers can leverage AI technologies like Keysight Eggplant Test to enhance their skills, boost productivity, and ensure the highest quality software. 3️⃣ Testing in the Age of Agile and DevOps: Learn how modern development approaches have transformed the role of testers and how they can adapt to remain essential in fast-paced environments. 4️⃣ Emerging Testing Strategies: Dive into the latest methodologies, such as context-aware testing and test-driven development, and explore their potential to improve test coverage and effectiveness. 5️⃣ The Growing Importance of Domain Knowledge: Discuss the significance of integrating domain expertise into the testing process and how AI can help testers gain valuable insights into specific industries. 6️⃣ The Ethical and Social Implications of AI-Driven Testing: Examine the ethical concerns and societal impact of AI in software testing, and envision a responsible and inclusive future for the industry. Don’t miss out on this thrilling journey into the future of software testing! Subscribe now to ”Test-Talks.com” and stay ahead of the curve as we redefine the boundaries of quality assurance. #TestingTomorrowToday #SoftwareTesting #FutureOfTesting #Podcast

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