Open source AI for Playwright, Pillars of Continuous Testing and more TGNS101

Want to know a new open source solution that brings AI to Playwright? What's the difference between AIOps versus MLOps? And what are some pillars to continuous testing? Find out in this episode of the Test Guild News show for the week of November 12th. So grab your favorite cup of coffee or tea and let's do this. But first, are you looking to take your automation projects to the next level? Look no further than applitools and the Visual AI validation platform. Trust me, if it is a game changer. Plus you can try it out for yourself by creating a free account now by using the special link in the comment down below and see the difference for yourself. So at the annual Mable Experience Conference, they unveiled three new features. The first is really no surprise. It's Generative AI Powered Auto healing and this feature enhances Mable's existing auto healing capabilities using a large language model to help reduce test maintenance by 95% and significantly boost productivity. The second new feature was browser load testing and this innovation simplifies performance testing, allowing teams to convert low code browser and API tests into performance tests, ensuring app performance aligns with actual user experience. This is a growing trend we're seeing more and more companies invest in this area for performance testing, using real browsers and leveraging existing automated tests as performance test. And the third innovation is mobile testing, so currently in a private beta and and this feature offers comprehensive and reliable automated test coverage for Android and iOS apps, enabling faster test creation and execution. So you probably heard a lot about AIOps and MLOPS, but you may not be sure of the differences. Well, I have a resource for you that goes over what the two are and what the key differences are and benefits of both. So I actually found this post on LinkedIn by Scott Moore posting to this article. So thank you Scott for letting me know about this resource and the article itself is what actually dives into AIOps and MLOPS and how they're often confused, but they serve distinct and critical roles when you're using them within IT and data operations. And this blog post sheds light on the differences between the two and approaches of their unique applications. So and it goes into detail on how AIOps applies AI machine learning to enhance and automate IT operations and some of the benefits it offers is proactive problem resolutions, automation of routine tasks and enhanced visibility of IT infrastructure, reduced downtime and cost optimization. And it also then compares it to MLOps, which is really streamlining machine learning, which focuses on deployment, monitoring and management of machine learning models in production. And some of its benefits is to help you ensure reproducibility, scalability, governance and reliability of machine learning models. And it also concludes by saying while AIOps and MLOps are different, they are not mutually exclusive and and their integration can create closed loop systems where AI driven insights from AIOps informs and automates actions within MLOps so they actually work together. All right, pretty much every time we talk about AI with automation tooling on this show it has to do with vendor based solutions. Well, I found a open source solution that just came out. It's in its infancy called autoplaywright, which is an open source project that integrates artificial intelligence into your testing workflow with Playwright. Let's check it out. So I first heard about this on a comment on my LinkedIn post by Luke who goes hey, I may find this interesting. I clicked on the link and said wow, I know you all definitely would find this interesting. And he also goes over how you definitely want to check out some other things he talked about on Reddit that explains it a little more in detail. And the link to GitHub goes over how you can use automating playwright steps using ChatGPT and how really streamlines your your testing workflow and simplifies the process of running Playwright tests using AI, making it more intuitive and effective. And so some key features of this particular solution is ease of use. So autoplayright allows for rapidly creating tests using simple plain text AI driven prompts. You can also use this tool to leverage OpenAI's technology to translate plain text instructions into actual testing commands. And autoplayright can handle various tasks from clicking links to performing complex queries and assertions. And like I said, this is really brand new and I think Luke is looking for input. So definitely give you input in the comment down below or I'll have them tagged within the post as well. So let them know features you'd like to see or improvements that could be made to autoplaywright. It's a great step forward for folks that are looking for open source solutions actually integrate AI to so thank you Luke for this new tool for the community. Appreciate it. Are you looking for ways to enhance your continuous testing in your cicd? Well I have a framework that can help you do this. I actually came across this article on my LinkedIn YouTube feed. It was a post by Amala that caught my attention how she talked about how this post by Baz helped her really realize the potential of her continuous testing efforts. And she links to Baz's article that he recently updated on supporting continuous testing with Fighter or fitr test automation and this model goes over focus testing, which is automated. Tests must target the right application components and layers, ensuring they are efficient and relevant. The second pillar is informative results. Test should provide clear, actionable feedback tailored to different audiences from developer to managers. The the third is trustworthiness. Reliability is key. Tests must accurately reflect the state of the software, avoiding false positives and negatives. And the fourth pillar is repeatable processes. So tests should be able to run on demand, necessitating robust strategies for test data environments. And it goes over some of the challenges and solutions such as different hurdles that you can use or to get over when you're doing continuous testing. Definitely a must read article by Boz that you should check out in that first comment down below, I also came across a new tool I haven't heard of before From Scott on LinkedIn and he talks about a project management visibility tool that they came out called Spec to Test AI. And this solution is a platform that transcends the limitations of traditional project management tools like jira, offering a comprehensive and intelligent approach to project visibility and management that I know a lot of testers and developers struggle with recurrent solutions and this article goes into detail on how Spec to Test AI helps with comprehensive requirement analysis like Unlike traditional platforms that simply create catalog user stories, Spec to Test AI conducts an in depth examination of requirements across five categories, ensuring clarity and alignment with business goals. It also leverages AI to analyze requirements in minutes. A task typically takes usually over an hour, providing feedback and optimization. It also helps with cybersecurity risk. It aids in developing robust security requirements and test cases from the start. It also has Automatically Test Case Generation, which generates prioritized test cases that address functionality, complexity and interdependencies with minimal user intervention. So if you haven't checked this out, if this is a tool that sounds useful for you, definitely check it out. Let me know about it in the comments below. Down below and this announcement goes over how Dynatrace announced a groundbreaking Kubernetes experience for platform engineering teams and this new offering empowers Dynatrace's advanced observability, security, AI and automation capabilities and its help set to transform how Kubernetes environments are managed and optimized. And it also has predictive maintenance. With AI leveraging Dynatrace's casual and predictive AI, the platform could automatically detect and forecast a anomalies in your Kubernetes clusters, and this can empower you to proactively address issues providing negative impacts on user experience and the new experience also supports key DevSecOps processes including automated quality gates and validation of builds, deployments and releases. And this really ensures a reliable, secure and scalable kubernetes environment, optimizing developer experience and testers experience. And if you're looking for a DevSecOps platform, I have another announcement and this one is from cloudbee and Clobby announced the launch of its new cloud native DevSecOps platform and this platform is powered by Amazon Elastic Kubernetes Services and it's set to redefine the standards of DevSecOps in the cloud. And some of the key benefits of this platform is as seamless AWS integration. It offers the ability to scale up or down transparently, ensuring both cost effectiveness and performance optimization. It allows for proactive security measures. It helps simplify workflow management. And this is really a trend. I see a lot more companies trying to bring companies more agility, security and versatility to enterprises, enable them to innovate continuously in an application and experience first world. And these are just two platforms that can help you do that and you can check them all in that first link down below. And for links of everything of value we covered in this news episode, head on over to links in that first comment down below. And while you're there, make sure to check out our awesome sponsor applitools free account offer and discover how to take your automation testing to the next level. Leveraging Visual AI so that's it for this episode of the Test Guild new show. I'm Joe. My mission is to help you succeed in creating end to end full stack pipeline automation awesomeness. As always, test everything and keep the good. Cheers.