Rainforest QA

Three unconventional software testing insights from Senior Staff Engineer AJ Funk on the latest episode of the Adventures in DevOps podcast: 1. "100% test coverage" is a myth. It's impossible to cover every edge case and combination. Instead, focus on testing core user workflows and critical paths (like login flows) rather than trying to test every possible input variation. 2. For microservices, you don't need to test every individual service interaction in your end-to-end tests. If the user-facing functionality works in end-to-end testing, it effectively validates that all the underlying microservices are working together correctly. 3. Don't be afraid of exposing your testing environment on a public URL. You already know running tests in production-like environments is crucial. AJ argues that teams should expose their QA environments publicly (assuming you're not exposing sensitive data), noting that if someone breaks your QA environment, you've discovered how they could have broken production without the actual damage. The fear of exposing non-production environments is often overblown.

Open-source tools may seem free, but they cost your team 20+ hours/week maintaining tests. Using no-code automation can speed up testing. Rainforest QA COO Lauren Harold explores smarter alternatives in this article.

For startups and teams of all sizes, E2E #testing is both critical to maintaining product quality, and one of the most painful processes in CI/CD. Lauren Harold of Rainforest QA offers #predictions for how the evolution of #QA will shape #devops in the coming year. https://lnkd.in/gwSQhCnD #GenAI #softwaretesting

Many startups face hidden time costs when relying on “free” open source testing tools. By Rainforest QA COO Lauren Harold | #OpenSource #DevOps

For startups and teams of all sizes, E2E #testing is both critical to maintaining product quality, and one of the most painful processes in CI/CD. Lauren Harold of Rainforest QA offers #predictions for how the evolution of #QA will shape #devops in the coming year. https://lnkd.in/gwSQhCnD #GenAI #softwaretesting

The hype for AI is real – some 81% of dev teams are using genAI in their software testing. But here’s the kicker: it may not *actually* be saving them time. In fact, for teams using open-source frameworks, it could be making it worse. That’s what we found through a new survey of 625 software devs and eng leaders on their automation practices. Teams using AI for test creation and maintenance in open-source frameworks are actually spending slightly *more* time on those tasks compared to teams not using AI. This doesn’t mean it won’t help, eventually. It’s still early days, and there’s no standard implementation of AI across open-source testing startups. Some implementations are certainly better than others, but these results suggest that teams are still trying to find the ones that work. Meanwhile, the data did show that one technology makes a *huge* difference in the time spent on automated test-maintenance: no-code. Regardless of team size, teams using no-code test automation tools spend significantly less time creating and maintaining automated tests compared to teams using open-source frameworks. They have faster release cycles and deliver code with more velocity. You can read all the details of our findings in our report. (Link in the comments.) And if you want to learn more about how no-code can help you, you know where to find me.

Thinking of automating your app’s E2E tests? Here’s the cheat code to avoiding the time-consuming pain of test maintenance. We surveyed 625 software developers and engineering leaders to learn about their test automation practices. The survey revealed that teams using no-code test automation tools spend significantly less time creating and maintaining automated tests compared to teams using open-source frameworks. In fact, small dev teams using no-code tools are 24% more likely to keep their automated test suites up to date than their open-source counterparts. ? That means fewer QA bottlenecks and more reliable tests that don’t return false-positive failures every time you run your suite. If you care about release velocity, you may want to consider a no-code test automation solution to reduce the time your team spends on QA. Learn all the details of our findings in our new report. Link in the comments.

We learned some unexpected things about AI when we surveyed 625 software developers and engineering leaders on their test automation practices. We assumed AI would be a net positive. That it’d help speed up the painful and time-consuming process of maintaining automated tests in open-source frameworks like Selenium, Cypress, and Playwright. ??But the results show that teams using AI for test creation and maintenance in open-source frameworks spend slightly *more* time on those tasks than their counterparts not using AI. ?? So does this mean AI is a dud in open-source testing? Not necessarily. There’s no “standard” implementation of AI across open-source testing setups. Some implementations certainly work better than others, but these results suggest that teams are still trying to find the ones that work. In the meantime, the data show there’s a less-hyped technology that makes a huge difference in the time spent on test automation. Teams using no-code test automation tools spend *much* less time creating and maintaining automated tests than the teams digging around in the code of open-source frameworks. For engineering leaders who care about shipping velocity, it looks like no-code is a big cheat code. You can read all the details of our findings in our report. Link in the comments.

AI hype is real —?81% of software teams are using AI in their testing workflows. ?? That’s just one of the things we learned when we surveyed 625 software developers and engineering leaders to learn about their test automation practices. The survey data answer many important questions for teams trying to increase their shipping velocity: ?? When do software teams transition from manual to automated testing? ?? How are software teams using AI in their testing workflows? ?? What's the real impact of AI on test creation and maintenance time? ?? Which technologies consistently speed up testing workflows? ?? How do test automation practices differ between smaller and larger dev teams? ?? How many teams are automating tests without the help of QA engineers? Get all these details and more in our new report, The State of Software Test Automation in the Age of AI. Link in the comments.

https://lnkd.in/gWUfvx-Y