AI LLMs in Testing, Cypress LockDown and more! TGNS98

Want to see an example of a practical application of AI in enhancing user engagement and experience? Or have you heard about the Crowdsource experiment that use large language models for testing? And what feature was just released that enhances web app testing using Microsoft Playwright? Find out in this episode of the Test Skill News show for the week of October 8th. 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 testing platform. Trust me, it is a game changer. Plus you could try it out for yourself by creating a free account now by using the special link in the first comment down below and see the difference for yourself. And let's kick things off. They follow the money segment. Let's check it out. All right, so in a significant development, APSERA is a leaning DevOps orchestration platform provider, has raised 12 million in Series A Plus funding. It's going to be used to help develop and expand their Hummingbird AI, which is a generative AI solution designed to optimize developer experience and productivity through the software lifecycle, offering unified insights and critical analysis across the entire software development process. It's equipped to identify bottlenecks and provide remediation strategies, transforming error codes into human readable summaries and actual insights. And this innovation is set to make cloud development faster, more secure and efficient across multi cloud operations. And I also came across an interesting blog post on how someone actually did an experiment using large language models like ChatGPT for testing. So let's check this out. So Vipul posted this blog article that goes in detail on exactly what was done. So this experiment focused on application of large language models and design test cases for banking software, specifically the Interest Adjustment Register Maintenance system. And the experiment involved using an AI model named Bard to generate test cases based on a screenshot of the software interface. Bard, with its AI power, was able to generate functional, negative and additional test cases, and the team was kind of impressed with the ability of this approach to discern the software's purpose and and the proposed relevant tests. The AI model could even provide detailed steps for creating, updating and deleting entries, and offered additional tests that delve into core banking aspects. This experiment opened up a discussion on the depth and breadth of AI's role in software testing, and it also raised some questions about the future of testing in the impending reality where AI could potentially handle a significant portion of test design and execution task. So kind of a hot take in this blog post. Let me know your thoughts in the comments and whether or not you think AI could potentially handle a large portion of test design and execution tasks. I also came across another real world example of a company that used AI to help them with developing a piece of software that they released to production. Let's check it out. So this article goes into how earlier this year Honeycomb IO, a pioneer in observability, launched an AI powered feature known as Query Assistant designed to enhance the user experience in data querying. So utilizing large language models, just like we saw in the other post, Query Assistant aids users in navigating and querying their data more effectively, aiming to bridge the gap for those unfamiliar with Honeycomb's interface. So how did it do? Well, Query Assistant has shown a strong correlation with manual querying retention. Teams using this feature are more likely to continue running manual queries, creating complex queries indicating a positive impact on user engagement and productivity activation. So just another cool case of how a company used AI in the real world and what the result was. And definitely you should read more about it in that first comment down below. And it just seems like every news article that came my way this week was around AI, and this next one is no exception. And this is all how powersoft introduced enhanced features to its continuous quality platform. And it's an integration of generative AI or Genai, to automate test case generation for APIs, ensuring accuracy and relevancy in real time. And this feature helps amplify application coverage visibility across diverse microservices, ensuring thorough and effective testing. And in the realm of web accessibility, PowerSoft helps simplify compliance with web content accessibility guidelines by reusing existing SoA test web UI test enterprises can now seamlessly integrate accessibility testing into their automation pipelines as well. So I announced this feature, I think a few weeks ago, but I have a more detailed article for you that goes over with more info. And this is all on how Playwright has announced a cloud based scalable solution for end to end testing of modern web applications. And this innovation promises faster, broader test coverage and addresses the challenges of the delayed feature delivery and quality assurance. So with this new feature you get some new benefits like better scalable testing, parallel execution, cross capabilities, seamless integration, a bunch of different things. And one of the key capabilities is the capability for development teams under pressure to deploy enhancements quickly, eliminating the need to selectively run only a subset of tests or executing them less frequently. All right, this next article came my way via Lisa Crispin as I was scrolling up my LinkedIn feed and it's on the state of DevOps so this is the state of DevOps report for 2023 from Google Cloud. Dora and the research explored three main outcomes. The first one is organizational performance, the second one is team performance and the third is employee well being. And some key takeaways are 1. Establishing a healthy culture building with users in mind infrastructure flexibility with the cloud balancing delivery speed, operational performance and user focus unlocking software delivery performance and faster code reviews distribute work fairly and amplify technical capabilities with qualified documentation and as I said, this goes into more and more detail on all these sections and other key findings. Really, really thorough and you definitely should check that out in that first comment down below by downloading the report and seeing it for yourself. So it's probably a trend I'm going to see more and more of. So that's why I brought it up. So in a significant move, Cypress has announced enhanced measures to safeguard its intellectual property. So starting with version 13 of the Cypress app, the company will enforce data recording restrictions to block certain third party products and services. This decision underscores the Cypress commitment to ethical business practices and fear competition. So Cypress was founded over nine years ago and has been one of the tools on the forefront of helping facilitate innovation and quality and application development. Testing the open source Cypress app and the commercial Cypress cloud have worked in tandem with the leader funding and sustaining the community focus model of the former. So despite these new restrictions, a vast majority of Cypress users probably won't even notice it. They'll remain unaffective and the company assures continued robust and reliable tool availability for all users and appreciates the ongoing support from the Cypress community. So this really impacts a lot of solutions I've seen lately that have open source dashboarding that kind of mimics what Cypress is doing with their PEED stuff. So definitely something to be aware of. All right. I'm not sure how many people actually do this, but I thought it was a in depth article that could help you if you are and it is how implementing and debugging load tests for GO applications running in Kubernetes and this was posted by the folks at K6 so in this article authored by Donald Lee provides insights and practical guides on implementing and debugging load tests for GO applications running inside of Kubernetes using tools like K6 IntelliPresence so Donald provides a hands on approach, uses a demo application to illustrate the process of setting up Kubernetes clusters, deploying an application and implementing load tests with K6. The article also highlights the use of telepresence to facilitate the debugging process, making it easier to trace performance issues. So if you're doing anything with load testing Performance testing, I think this is something interesting, especially if you're trying to get things running in Kubernetes or a go. Definitely a must read. You can check it out in that first comment down below. So if you do performance testing, you probably are familiar with JMeter. So this next article is about mastering API testing with Catalan Studio and JMeter, and this guide is great for both seasoned testers and beginners. It offers a step by step walkthrough from creating an API request in Catalan Studio to executing and validating them with JMeter. The author also outlines the process of setting up proxies and recording tests, including ensuring readers are well equipped to navigate the complex landscape of API testing. All right, for links of everything of value we covered in this news episode, head on over to all the 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 Skill News Show. I'm Joe. My mission is to help you succeed in creating end to end full stack full full pipeline automation osmos. As always, test everything and keep the good Cheers.