TestGuild Automation Podcast

AI is changing how we build and test software, but most teams are still struggling to turn AI-generated tests into real production value.

Use code TESTGUILD3 try for yourself free now for 3 months: https://links.testguild.com/Kobiton

In this episode, we break down what actually works when it comes to AI-powered mobile test automation, especially when running tests on real devices not simulators from Claude.

• How teams are generating and running Appium tests using natural language in minutes
• Why AI-generated tests often fail—and how to avoid costly false positives
• The real impact of AI on test automation roles and responsibilities
• How real device testing exposes issues AI alone can't catch
• Practical ways to reduce test maintenance while increasing coverage

We also explore a major shift happening right now: AI is making it easier to create tests—but dramatically increasing the volume of code and risk that needs to be validated.

That means one thing: Testing isn't going away—it's becoming more critical than ever.

If you're a QA engineer, automation engineer, or DevOps leader trying to keep up with AI-driven development, this episode will give you a clear, practical perspective on what to focus on next.

AI coding tools are helping teams move faster than ever, but there's a hidden cost.

In this episode, we break down new insights from a DevOps industry report revealing a growing "velocity paradox": teams are shipping more code, but experiencing more failures, rollbacks, and burnout.

You'll discover why AI adoption is heavily skewed toward coding, but not testing, pipelines, or observability, and how that imbalance is creating fragile systems that break under pressure.

More importantly, you'll learn what high-performing teams are doing differently to maintain quality while scaling speed.

✔️ Why AI is increasing deployment failures (and how to stop it) ✔️ The "velocity vs quality" trap hurting modern DevOps teams ✔️ How to reduce flaky tests and pipeline instability ✔️ Why observability and feature flags are now critical, not optional ✔️ Practical ways to improve your CI/CD pipeline for AI-driven development ✔️ The role of QA engineers in the age of AI (and why it's growing, not shrinking)

If you're a tester, automation engineer, or DevOps leader trying to keep up

AI is changing how we build and test software, but most teams are struggling to turn that promise into real results.

In this episode, we break down what it actually takes to scale quality engineering across global teams without creating bottlenecks, burnout, or broken processes.

You'll learn:

• why most test automation and transformation initiatives fail
• how to separate AI hype from reality
• what high-performing teams are doing differently to ship faster with confidence

Today's expert, Sunita McCoy, a Global Engineering Leader and Transformation Specialist, shares practical insights from leading large-scale engineering transformations, including:

• how to build a culture that supports AI adoption
• why "quality as a phase" is dead
• how to shift toward treating quality as a product

If you're a QA leader, automation engineer, or DevOps professional trying to improve reliability, reduce risk, and future-proof your skills in the age of AI, this episode gives you a clear path forward.

Mobile test automation is still one of the biggest bottlenecks in modern software delivery. In this interview, QApilot's Co-founder Aditya Challa explains why most AI testing approaches fail and how to fix them.

Learn more about QApilot: https://links.testguild.com/flutterqa

If your mobile tests are flaky, slow, or hard to trust, you're not alone.

Most teams are trying to apply LLM-based AI to problems that actually require deterministic reliability—and that's where things break down.

In this video, you'll learn:

• Why mobile test automation breaks at scale
• The real issue with "99% accurate" AI in testing
• LLMs vs deterministic AI (and why it matters for mobile apps)
• How flaky tests destroy confidence in your pipeline
• How QApilot approaches mobile testing differently
• What reliable, scalable mobile automation should look like

What this means for you:

Fewer false positives, faster releases, and mobile tests you can actually trust.

00:00 Why Mobile Test Automation Is Still Broken 01:10 QApilot Overview 01:51 Why Mobile Testing Tools Fail 03:13 Why Appium Isn't Enough 05:09 QApilot's Approach to Mobile Testing 07:10 Scaling Mobile Testing Across Devices 08:02 Autonomous Testing + Human in the Loop 10:55 How QApilot Works (Architecture + Agents) 13:45 Real Example: Mobile App Crawling in Action 16:31 Finding Bugs Automatically (Performance + Accessibility) 18:52 Device Farms & Real Device Testing 21:50 Future of Mobile Testing (SRE + AI + Quality Layer) 27:06 Real Customer Results & Case Study 31:02 Why QApilot Focuses Only on Mobile 34:04 Where QApilot Fits in CI/CD 36:00 How to Try QApilot + Final Advice

Are you the only tester on your team—and expected to ensure quality across everything?

In this episode, we break down the growing challenge of solo QA testing in the age of AI-driven development—where code is generated faster than ever, but confidence hasn't caught up.

Christine Pinto shares real-world insights from her experience as a solo tester and now as a founder building tools designed to help testers reduce risk, collaborate better, and make smarter release decisions.

You'll learn:

Why "all tests passing" doesn't mean your product is safe The hidden risks of AI-generated code and test automation How to shift from test coverage to risk-based testing Practical ways solo testers can avoid burnout and isolation How to bring collaboration back into QA—even if you're the only tester Why better requirements still matter more than better AI

What if your production logs could automatically generate new test cases?

In this episode, Joe Colantonio sits down with Tanvi Mittal to break down how AI-powered log mining is changing the way teams approach software testing, quality engineering, and DevOps.

Most teams ignore production logs or use them only for debugging. But those logs contain real user behavior, real failures, and real edge cases—the exact scenarios your test suite is probably missing.

👉 Learn how to:

• Convert production logs into automated regression tests
• Use AI to detect real-world failure patterns
• Apply shift-right testing to catch bugs earlier (and smarter)
• Handle the challenge of testing non-deterministic AI systems
• Reduce flaky tests and automation debt with real data

If you're working with Playwright, Selenium, Cypress, or AI-driven testing tools, this episode will give you a completely new way to think about test coverage.

How do you ensure software quality when the system you're testing doesn't give the same output twice?

Go to https://links.testguild.com/inflectra and start your free 30-day trial, no credit card, no contract required.

That's the core challenge facing every QA team building or testing AI-powered applications today and it's breaking all the rules we've relied on for decades.

In this episode of the TestGuild Automation Podcast, I sit down with Adam Sandman, co-founder of Inflectra, to get into what non-deterministic AI testing actually means in practice, why traditional pass/fail testing no longer cuts it, and what quality professionals need to do differently right now.

We cover:

• Why AI-generated code is raising the stakes for QA teams while budgets stay flat
• The fundamental difference between deterministic and non-deterministic systems — and why it changes everything about how you test
• How to set acceptable risk thresholds for AI systems (hint: it depends on whether you're building an e-commerce chatbot or an air traffic control system)
• Why testers who embrace AI as a tool — not a threat — will be the ones leading their organizations forward
• How a live demo failure at a conference inspired Inflectra's new non-deterministic testing tool, SureWire

If you're a tester, QA manager, or automation engineer trying to figure out how to keep up with AI-driven development without losing your mind — or your job — this one's for you.

What does it really take to build a test automation tool that millions of testers rely on, without venture capital, paid ads, or a massive team?

In this episode, we explore how SelectorsHub grew into one of the most widely used productivity tools in software testing, reaching over 1.6 million testers worldwide.

You'll discover:

• How to build test automation tools that solve real QA pain
• Why community-driven development beats chasing funding
• How to prioritize features when you have thousands of users
• Whether AI testing tools will replace selector-based automation
• How to choose between Playwright vs Selenium using automation analysis
• What founders and QA leaders can learn from scaling without VC

If you're an automation engineer, QA lead, DevOps professional, or tool builder looking to scale smarter, this episode delivers real-world insight without hype.

Whether you're building frameworks internally or launching your own automation product, you'll walk away with a clearer strategy for solving problems testers actually care about.

AI test automation is evolving fast — but most tools still generate brittle code that breaks with every UI change.

See it for yourself now: https://links.testguild.com/Thunders

In this episode of the TestGuild Podcast, Joe Colantonio sits down with Karim Jouini, founder of Thunders, to explore a radically different approach to AI testing: executing test automation in plain English without generating Selenium or Playwright code.

Instead of "auto-healing selectors," Thunders interprets natural language directly — allowing teams to:

• Ship twice as fast
• Achieve 10x test coverage with the same resources
• Reduce regression cycles from weeks to days
• Eliminate massive automation maintenance overhead

Karim shares real-world case studies, including:

• A European bank that reduced a 3-year core banking upgrade testing effort to 4 months
• A SaaS company that transitioned from a traditional QA team to AI-assisted product-led testing

We also discuss:

• Whether AI test agents replace QA roles
• How QA managers must shift from individual contributors to AI managers
• The risks of adopting AI without a defined success metric
• The future of shift-left testing in the AI era

If you're a software tester, automation engineer, QA lead, or DevOps leader trying to understand what's hype versus real ROI in AI testing — this episode breaks it down.

Try it for yourself and see how AI testing fits into your pipeline.

Get personal demo: https://links.testguild.com/Thunders

Is traditional performance testing becoming obsolete?

In this episode, performance engineering expert Akash Thakur shares why AI is fundamentally transforming load testing, scripting, observability, and shift-left strategies.

With 17 years of real-world enterprise experience, Akash explains how AI-augmented tools are already reducing scripting time by 30%, improving analysis speed, and helping teams move from reactive performance testing to predictive intelligence.

You'll learn:

• How AI is accelerating performance scripting and analysis
• Why shift-left performance testing is finally becoming realistic
• The role of structured data in predictive QA models
• How to test AI applications (LLMs, GPUs, inference throughput) differently than traditional web apps
• What the future role of performance engineers looks like — architect, not script writer

If you're a performance tester, SRE, QA leader, or DevOps engineer wondering how AI will impact your role — this episode gives you practical, actionable insights you can apply immediately.