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Outsourcing is an affordable way to scale regression testing, freeing up developers to focus on shipping code. However, we’ve surveyed engineering leaders and developers and found that outsourcing regression testing to the wrong service provider can lead to several issues: As a result, we recommend evaluating how a regression testing service provider addresses these three issues before partnering with them.

Software and quality assurance teams use AI in all parts of the automated testing workflow. According to a survey of 625 software developers we ran, 81% teams use AI tooling in their testing workflows for some variety of test planning, test management, test writing, and even analyzing test results. But AI can make the biggest impact on the most time-consuming steps in the automated testing process: test creation and maintenance.

For software teams committed to using open-source test automation frameworks, LambdaTest is a solid one-stop shop for everything you need in the testing workflow. In this piece, we’ll give you some other options that also operate around open-source frameworks. But if you’re looking to automate your manual tests and haven’t already invested in open source, you should know about the painful tradeoffs that come with using solutions like Selenium, Cypress, or Playwright. For example.

When you want to expand your QA testing capabilities or resources, Testlio can recruit QA specialists from their large “crowd” of QA personnel. Testlio also provides a platform to manage your tests, test runs, and results. If you’re considering a platform-enabled QA service like Testlio, there are a handful of other noteworthy providers you should know. We’ll cover them in this piece.

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.