TestGuild Automation Podcast

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.