Rainforest QA的动态

We just released the an awesome industry report: The State of Software Test Automation in the Age of AI

Will AI Really Replace All Developers & Testers Work? Sam Altman recently hinted at AI’s potential to take over development roles sooner than expected. But what does this mean for software testing? Join Testleaf's Gen AI Testing Master Class to explore how AI is reshaping testing, making processes smarter, faster, and more reliable. Get ahead of the curve—discover the future of testing with AI! Webinar link : https://lnkd.in/g-RMKSbF

Ever wish you could predict the future? With AI, businesses kind of can! AI doesn’t just understand current customer patterns; it predicts what they’ll do next. By using machine learning, AI can forecast things like which customers might stop buying (that’s churn), future shopping habits, and how people will react to new marketing campaigns. These insights let businesses get ahead of the curve, tweaking their strategies to keep customers coming back and to seize more sales opportunities. Want to see how AI is turning these predictions into reality? Swing by S2udios.com to get the full scoop!

Title: “AI Automation with GitHub: What’s Next for Business Deployment?” Discussion Prompt: "Now that we’ve explored setting up automation in GitHub, let’s look ahead. As AI continues to evolve, how do you see AI automation impacting deployment in the next 5 years? Are there new AI tools or trends you’re excited about incorporating into your workflows? Feel free to share your thoughts, upcoming trends, or even challenges you’re facing with AI and automation!"

Great discussion prompt! AI automation, particularly when integrated with platforms like GitHub, is already transforming the way we approach deployment, but the next 5 years will likely push those boundaries even further. One of the most significant impacts I foresee is increased deployment speed and precision. As AI tools continue to evolve, they will become better at predicting potential bugs, optimizing code, and managing complex dependencies. This will allow teams to automate not only the deployment process but also anticipate and resolve issues before they affect the end-user experience. Trends and Tools to Watch: AI-Driven Continuous Deployment (CD): We're moving towards a scenario where AI will be able to monitor the entire pipeline—from code commits to production—automatically deciding the best time to deploy based on real-time data (such as traffic, performance, and user behavior). AI-Powered Code Reviews and Quality Assurance: Tools like DeepCode and Codacy are just the beginning. In the next few years, I expect AI to play a more central role in analyzing code quality, suggesting optimizations, and even automating patches for security vulnerabilities. Predictive Analytics and Resource Management: With AI becoming more adept at resource allocation, we’ll likely see better cost optimization in cloud deployments. AI could forecast the resource needs of an application based on historical data and automatically scale servers, reducing operational costs while ensuring performance. Challenges: While these advancements are exciting, they also pose challenges, such as the need for robust AI governance and the risk of over-reliance on automation. Ensuring that AI models making deployment decisions are transparent, secure, and free from biases will be critical to maintain trust and control in business environments. I’d love to hear others’ thoughts on how they’re preparing for these shifts and any AI tools they’ve found particularly useful for automation within GitHub workflows!

Failed deployments? We got an AI for that. We’re excited to announce a brand new AI feature designed to troubleshoot failed deployments. ?? Why This Matters? Failed deployments can be a major setback, costing precious time and resources. Our new AI-driven feature minimizes these disruptions by providing immediate, actionable insights to swiftly fix issues, ensuring smoother and more reliable deployment processes. This enhancement reflects our investment in advancing the developer workflow with world-class developer tooling and AI features. Learn more here: https://ntl.fyi/3Pd1yGe

Your new favourite Netlify feature. My team created the first version of this for our internal "Jamhack" and I've used it daily ever since. Once you try it, I'm confident that you'll use it constantly too. It's one of those features that even I didn't truly get until I'd built the first version and actually tried it. It was so compelling to use that everyone on the team switched their day-to-day Netlify app usage to the deploy preview that had it available until it was available to everyone internally. The real surprise was that the main use isn't actually diagnosing the build failure - it's saving time scrolling through and reading all the logs, trying to find the most salient bits. This extracts all the important parts and gives a diagnosis too.

A napkin doodle of the upcoming transition between Human-Driven and AI-Driven testing. Note: the transition might be faster, but general curves and proportionality are likely. https://lnkd.in/gaGTBcDH. #AIAmorous Inflection Points: ~2025: AI Makes Developers more productive, demand for Software Eng starts dropping ~2025: Demand for Human Testers spikes a bit--More code generated, and More escaped issues ~2026: AI-driven testing starts exponential increase in test coverage. ~2028: More code generated by AI than Humans, continued slow dropoff in demand for Human Software Engineers ~2030: More testing performed by AI than Humans, start drop in demand for Human-testing. ~2032: AI generates more coverage than actually needed ??♂? ~2033: AI Generates about 3X more Code and Test coverage vs Humans ~2036: Rate of AI-generated code and test coverage plateaus --we have 'enough' software and maintained by AI. ~2037: Amount of generated code starts dropping as most software is written for AI, tested by AI, and is "AI".

AI-Powered Test Automation ? Case in point: ? Our client achieved, ? 60% reduction in test case creation time ? 30% faster release cycles ? 100% adherence to sprint deadlines ? 40% increase in testing coverage Gone are the days of manually creating test cases, constant pressure to deliver faster, and lengthy development cycles. ? Thanks to AI! ? Here’s how we flipped the script on their testing workflow: 1. Implemented intelligent AI test generation 2. Seamlessly integrated with existing development tools 3. Automated test case creation from requirement analysis Curious to hear what other testers think about AI's impact on test automation! #artificialintelligence #ai #testautomation #qualityengineering

How we Build AI Agents at OpenFunnel: Start Manually ??? and Iterate ?? One of the key things we've learned building AI agents is that the best way to get it right is to start manually. Here’s how we approach it: We first do the task ourselves—sometimes even screen recording the entire process to capture every detail. This helps us reflect on the tools we used, the inputs we gave, the parameters that mattered, and where we had to go back and adjust things. ?? This exercise is crucial because it helps us prove to ourselves that the task can be done manually. That gives us the confidence to build an agent to handle it, no matter the challenges. ?? From there, we work on replicating the task with code. This manual-to-automation loop surfaces all the edge cases you don’t anticipate at first. With each iteration, the agent becomes more reliable. ?? And here’s the important part: Don’t stress about how good the models or libraries are right now. It doesn’t matter if the orchestration tools, frameworks or the models are perfect. Just start. Trust the process. These tools—and your team—will keep getting better as you iterate. ? We’ve moved beyond simple prompting where you just describe what you want. Building good agents requires you to understand not just your flows but also your users’ flows deeply. So, like PG said, talk to users and write code :) ?? TLDR: Start with yourself, know your process, record it if needed, build it out, iterate, and refine. The future of AI is in understanding flows—let the agents handle the rest. #AI #AgentDevelopment #Automation #Iteration #BuildToLearn