Shifting left to put more on developers? They you have to find the right balance between automation and simplicity. Otherwise, you'll end up with complexity that slows everything down instead of speeding it up. @David Morgenthaler says tools like static analysis and AI-driven tests are shaping the future of software quality: Watch the latest episode of Test Case Scenario to explore how automation can streamline processes without overcomplicating development. It’s working for @Indeed.

What does it really mean to shift left, and are we overburdening our developers in the process? In this episode, Jason Baum, Marcus Merrell, and Evelyn Coleman sit down with David Morgenthaler, a Software Engineering Manager at Indeed, to explore the real-world implications of shift-left testing. Together, they dissect the challenges of integrating continuous quality into CI/CD workflows and the potential pitfalls of placing more responsibility on developers' shoulders.

57% of consumers are willing to switch to a competitor after three or four negative interactions. And that's being generous—sometimes it only takes one or two. Companies can’t afford to ignore red alerts anymore. Take Amazon's checkout flow, for example. It always works because they prioritize testing it rigorously to ensure their core business can generate revenue, even if other features are less polished.

In the last decade, we've leaned heavily on VC-backed tools like @Sumo Logic and @Splunk to help us sift through endless data and logs. These tools are invaluable, but now we're drowning in data, making it tough to focus on what truly matters. This challenge is no different in software testing. Here, many face the dilemma of running thousands of tests nightly with hundreds failing. It’s a sea of alerts and noise, leaving us questioning what we're actually testing.

Office Hours: Optimizing Test Execution Speed Are slow tests slowing you down? Watch this pre-recorded presentation from our Sauce Community Office Hours session where Diego Molina, Developer Advocate, dives into the best practices for optimizing test execution speed!

Ever thought about the power of glanceable and audible information? One team used a pattern of white noise—crickets—to indicate their site's status. When something went wrong, the crickets stopped, and everyone immediately knew to take action. It’s a brilliant way to process information passively and focus on what truly matters. Check out the most recent episode of Test Case Scenario as we discuss how to cut through the noise and highlight what’s truly important.

Drowning in notifications? Prioritize what truly matters. @Marcus Merrell asked himself, "Will I stop for any alert from this app?" Only three made the cut. Apply this to software testing: "Will we stop the shipment if this test fails?" If not, that test isn't helping. Don’t be afraid to delete unnecessary code. Quality control and assurance are crucial.

(Struggling to keep up with the relentless stream of notifications? You're not alone. In this episode of Test Case Scenario, Jason Baum and Marcus Merrell explore the impact of alert fatigue and signal overload on productivity and quality.

When it comes to software testing, context matters more than just numbers. @Titus Fortner sheds light on why meaningful metrics are essential for accurate risk assessment. Here's why this matters: In-Depth Understanding: Move beyond simple code coverage to understand the actual state of your system. Contextual Testing: Avoid assumptions and test the real-world scenarios to catch hidden bugs. Effective QA: Ensure that your metrics provide valuable intel, not just surface-level data.

Shift-left testing isn't about overloading developers with QA tasks—it's about strategic integration. @Titus Fortner explains the critical difference between true shift-left testing and merely piling QA duties onto developers: Early Integration: Having a dedicated QA integrated early into the process helps prevent defects. Specialized Expertise: Shift-left testing utilizes specialized QA professionals who have the deep knowledge and skills needed for effective testing.