Our new research shows AI adoption is high across software testing workflows. But AI isn’t (yet) paying off in ways that matter to software teams using open-source testing frameworks like Selenium, Cypress, and Playwright. Open-source teams using AI are still spending just as much — if not more — time on painful test writing and maintenance tasks than ones not using AI. Contents Toggle.

We sometimes have customers tell us they want to run end-to-end tests as often as possible in their development processes — as often as every commit. When you really care about quality, this might seem like a reasonable idea. After all, doesn’t the principle of shift left tell us to test as early as possible in the software development lifecycle? To catch bugs and other issues when they’re the least-expensive to fix?

When you’re ready to automate your manual tests, you might naturally think you need to hire someone with a technical skill set who specializes in automating end-to-end tests. That is, you might think you need to hire a QA engineer. It’s not an unreasonable assumption. But for many startups, it’s the wrong thing to do. QA engineers are quite expensive (in more ways than one), bottleneck release processes with their complex tooling, and can present other types of business risks.

If you’re one of the cool kids building AI-based products you’ve probably heard of — or are already doing — RAG. If you’re not, let me tell you what RAG is before telling you one weird fact about it. “Retrieval-Augmented Generation” is nothing more than a fancy way of saying “including helpful information in your LLM prompt.” Still, there are many ways to do it and many questions to answer when building a RAG pipeline.

We’ve integrated generative AI features deeply into our no-code test automation platform, Rainforest QA. Each of these features is designed to help you avoid the time-consuming and otherwise annoying work of keeping automated test suites up to date — so your software development team can stay focused on shipping, fast. In this video, our CEO, Fred Stevens-Smith, walks through what some of these genAI features look like in action.

When you’re ready to make the transition from manual testing to test automation, it’s natural to consider hiring. A QA engineer — who has the technical skills to write and maintain automated tests in an open-source framework — can take the burden of end-to-end test management off of your development team, allowing them to ship more code, faster. But hiring a good QA engineer is — often prohibitively — expensive.

Is your startup operating with no QA team and trying to figure out your options for improving software quality? Perhaps you’re proactively thinking about your QA process before quality becomes a problem. Our maybe quality issues have forced you to finally address your QA process (or lack thereof). Either way, end-to-end (e2e) automated software testing is always part of the answer.

Over a decade of helping startups improve quality, we’ve arrived at a set of QA testing best practices that work for teams shipping fast and frequently. They fit neatly into five organizing principles: Together, these principles represent The Rainforest Method. We’ve built our QA solution to make it easy for you to put this software testing method into practice and develop a quality product. A few notes: Contents Toggle.

If you’ve tried building real-world features with AI, chances are that you’ve experienced reliability issues. It’s common knowledge that AI makes for great demos, but… questionable products. After getting uncannily correct answers at first, you get burned on reliability with some wild output and decide you can’t make anything useful out of that. Well, I’m here to tell you that there’s hope.

Our goal has always been to fix QA. With today’s release, we’re closer than anyone else to doing it. Tests in Rainforest now fix themselves, creating more reliable results while allowing your team to focus on what matters — shipping code. Everyone has to do QA, but everyone hates doing QA. That’s why we started Rainforest in 2012. We make tools that make QA suck less.