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

[00:00:00] Joe Colantonio Want to know a new open-source solution that brings A.I. to Playwright? What's the difference between A.I. Ops versus ML Ops? 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. Grab your favorite cup of coffee or tea and let's do this.

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[00:00:35] Joe Colantonio At the annual Mabl Experience Conference, they unveiled three new features. The first is really no surprise. It's Generative AI-powered auto-healing, and this feature enhances Mabl'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 to 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 tests. And the third innovation is Mobile Testing. Currently, in a private beta. This feature offers comprehensive and reliable automated test coverage for Android and iOS apps, enabling faster test creation and execution.

[00:01:33] Joe Colantonio You've probably heard a lot about AI Ops and ML Ops, 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 the benefits of both. I actually found this post on LinkedIn by Scott Moore's posting to this article. Thank you, Scott, for letting me know about this resource. And the article itself is what actually dives into AI Ops and ML Ops and how they're often confused, but they serve distinct and critical roles when you're using them within I.T. and data operations. And this blog post sheds light on the differences between the two in approaches to their unique application. And it goes into detail on how AI Ops applies AI Machine learning to enhance and automate IT operations. And some of the benefits it offers are proactive problem resolutions, automation of routine tasks enhanced visibility by I.T infrastructure, reduced downtime, and cost optimization. And it also then compares it to ML Ops, 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 the reproducibility, scalability, governance, and reliability of machine learning models. And it also concludes by saying, while AI Ops and ML Ops are different, they are not mutually exclusive and their integration can create closed-loop systems where AI-driven insights from AI Ops inform and automate actions within ML Ops so they actually work together.

[00:03:01] 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 an open-source solution that just came out in its infancy called Auto-Playwright which is an open-source project that integrates artificial intelligence into your testing workflow with Playwright, Let's check it out. I first heard about this on a comment on my LinkedIn post by Luc who goes, Hey, I may find this interesting. I clicked on the link. It 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 explain 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 testing workflow and simplifies the process of running Playwright test using AI, making it more intuitive and effective. And so some key feature of this particular solution is ease of use. So Auto Playwright 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 Auto Playwright 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 Luc is looking for input, so definitely give your input in the comment down below or I'll have him tagged within the post as well. Let him know features you'd like to see or improvements that could be made to Auto Playwright. It's a great step forward for folks who are looking for open-source solutions that actually integrating AI. So thank you, Luc, for this new tool for the community. Appreciate it.

[00:04:45] Joe Colantonio Are you looking for ways to enhance your continuous testing in your CI/CD? 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 Amalya that caught my attention. She talked about how this post by Bas helped to really realize the potential of her continuous testing efforts, and she links to Bas's article that he recently updated on supporting continuous testing with FITR or FITR test automation. This model goes over focus testing, which is automated tests that must target the right application components and layers, ensuring they are efficient and relevant. The second pillar is a formative results test that should provide clear actionable feedback tailored to different audiences from developers to managers. The third is trustworthiness. Reliability is key. The test must accurately reflect the state of the software, avoiding false positives and negatives, and the fourth pillar is repeatable processes. Test should be able to run on demand, necessitating robust strategies for test data environments. And it goes for 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 Bas that you should check out in that first comment down below.

[00:06:02] Joe Colantonio I also came across a new tool I hadn't heard of before from Scott on LinkedIn. 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 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. The task typically takes usually over an hour to provide feedback and optimization. It also helps with cybersecurity risk needs 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 the tool that sounds useful for you, definitely check it out and let me know about it in the comments down below.

[00:07:21] Joe Colantonio This announcement goes over how Dynatrace announced a groundbreaking Kubernetes experience for platform engineering teams. And this new offering empowers Dynatrace as advance observability, security AI, and automation capabilities, and it helps set to transform how Kubernetes environments are managed and optimized. And it also has predictive maintenance with the AI leveraging Dynatrace as casual and predictive AI, the platform could automatically detect and forecast anomalies in your Kubernetes clusters, and this can empower you to proactively address issues preventing 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 the developer experience and tester's experience.

[00:08:12] Joe Colantonio And if you're looking for a Devsecops platform, I have another announcement and this one is from CloudBee. CloudBee announced the launch of its new Cloud native DevSecOps platform, this platform is powered by Amazon Elastic Kubernetes Services and is set to redefine the standards of DevSecOps in the cloud. And some of the key benefits of this platform is it has 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 enabling them to innovate continuously in an application inexperienced first world. And these are just two platforms that can help you do that. And if check them all in that first link down below.

[00:09:12] For links to everything we covered in this News episode. Head over to the links in the 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 News Show, I'm Joe, and 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.