Systems | Development | Analytics | API | Testing

AI has made waves in testing, but how much has really changed? In this episode of Test Case Scenario, Jason Baum, Evelyn Coleman, and Marcus Merrell take a critical look at the progress of AI in software testing. They discuss the real capabilities of tools like ChatGPT and Copilot, what’s improved over time, and what still leaves testers frustrated. You’ll also hear insights into how AI tools are shaping software development workflows, why cost and sustainability are becoming bigger concerns, and what testers can do to separate AI hype from practical solutions.

Why did @eMoney Advisor choose Sauce Labs? When you need a one-stop solution for device testing and automation, you can’t compromise. Here’s why Sauce Labs stood out: The platform offers testing across a wide range of devices, ensuring comprehensive coverage with minimal effort. eMoney Advisor was able to automate their testing processes, cutting down on time and boosting efficiency. It allows teams to scale their testing efforts effortlessly, ensuring smooth growth with no testing bottlenecks.

Adapting to hybrid work meant rethinking mobile testing. @Lori Henderson and @Chuan Au share how their team overcame the challenges of remote collaboration by redefining their testing approach. Here’s what made it work: This innovative strategy is setting the standard for modern mobile QA in a remote-first world.

How do you test an app used globally—from rural areas with bad connections to the users in cities with the latest devices? At @BlaBlaCar, Quality Assurance Manager, @Rémy Gronencheld explains their approach: Use low-connectivity simulations. Testing apps for offline access and slow networks ensures no user is left behind. Have device diversity. From high-end smartphones to aging devices, BlaBlaCar tests for every user scenario.

How do you scale testing while keeping up with a fast-growing product? @At BlaBlaCar, Quality Assurance Manager @Rémy Gronencheld introduced a “Q-as-a-Service” model that redefines how QA operates. Instead of a one-size-fits-all approach, the QA team adapts to the company’s needs—whether that means automating tests, mentoring teams, or optimizing workflows. By creating flexibility, Q-as-a-Service ensures quality becomes everyone’s responsibility while still providing expert guidance where it’s needed most.

Automation is like slicing through butter—smooth and time-saving. But how can you maximize your results without driving your developers up the wall? @Lori Henderson shares how they’ve boosted first-time releases and decreased hot fixes by blending automation with peer testing. Trust us, less manual work + quality throughput = happy developers.

Shift-left testing gets all the attention, but it’s shift-right that reveals what truly works. At @BlaBlaCar, Quality Assurance Manager @Rémy Gronencheld explains why testing in production is critical for real-world success: Shift-left: Build with confidence but rely on assumptions. Shift-right: Test against the unpredictable—low connections, real devices, and user behavior. The reality? Combining both approaches lets teams take calculated risks without sacrificing quality.