Author: Kevin Surace

In a marketplace flooded with “AI-washed” claims, distinguishing real generative AI from superficial automation is more critical than ever—especially in the high-stakes realm of end-to-end software testing. For organizations evaluating AI-powered testing platforms, asking the right questions can uncover massive differences in capability, scale, and ROI. At Appvance, we’ve engaged with hundreds of QA and

The rise of “generative AI” in software testing has sparked excitement across the industry—but it’s also led to widespread misconceptions. One of the most persistent myths? That the mere presence of generative AI means faster testing and higher productivity. In reality, some so-called generative AI implementations actually slow you down. A prime example: AI-driven assistants that let you type

Why are most software bugs still found by users after release? Because the industry still relies on outdated QA practices—manual testers, record-and-playback tools, and endless script writing. These approaches are slow, shallow in coverage, and deeply reliant on human capacity. The result? Missed bugs, late releases, and costly production issues. Appvance changed that equation years

Let’s be honest: traditional test automation was never truly automated. Writing scripts manually—or even recording them—has always been human-driven, slow, and prone to maintenance nightmares. That ends with AI Script Generation (AISG). AISG flips the script—literally. Instead of relying on testers to decide what to cover, it uses advanced AI models to learn your entire

AI copilots sound like magic: type what you want, and they “help” build tests. But here’s the dirty secret: for experienced QA engineers, copilots often slow you down. Typing instructions into a prompt instead of simply recording steps can be 2x slower. Worse, copilots generate partial test coverage, leaving senior testers to reverse-engineer gaps later.

For decades, QA has been the silent bottleneck in software delivery—manual, slow, and costly. Even with test automation tools, enterprises still spend 60–70% of QA time writing, editing, and maintaining scripts. Worse, despite all that effort, critical bugs still slip into production, where they cost exponentially more to fix and erode customer trust. But AI-first

There’s a dangerous myth circulating in the QA industry: that any AI is good AI. Tool vendors are racing to slap on “AI” features—copilots, agents, test case creators—all in an effort to look modern. But beneath the flashy UI and prompt-driven wizardry is a hard truth: these tools are actually slowing down experienced QA professionals. And there’s now

Ask any QA leader about test automation and you’ll hear the same pain points: script creation takes too long, test maintenance is constant, and coverage is never quite enough. AI has started to help—but most solutions are still limited by one fundamental bottleneck: the speed and complexity of the live application itself. At Appvance, we broke

In recent years, the software development lifecycle has been revolutionized by AI-driven coding assistance. Developers can now generate entire blocks of code from simple natural language prompts, turning abstract ideas into working software at unprecedented speed. This phenomenon is known as vibe coding—a creative, intuitive style of programming where ideas flow seamlessly from mind to machine,

Rethinking Software Quality in a Rapidly Evolving LandscapeAs businesses innovate and expand, the number of applications that support critical functions is growing exponentially. However, the complexity of these systems means that traditional QA methods are struggling to keep pace. In today’s fast-moving digital environment, relying solely on manual testing and legacy automation tools is no