Open Source AI for Testing, Playwright API Mocking, Top Testing Events and more! TGNS146

How can you use open source AI to enhance your automated test? Do you know how to mock API data with Playwright? And why should you attend test conferences in 2025? Find out in this episode of the Test Guild News show for the week of January 19th. So grab your favorite cup of coffee or tea and let's do this while we get into it. If you haven't already, please make sure to subscribe to Our Test Guild LinkedIn New Show Newsletter that I'll have a link for down below to never miss another episode. First up, our top testing conferences for 2025. I just released my latest blog post, so if you're a software tester looking to stay ahead of your career, you should consider attending industry conferences which offer opportunities to learn, network and stay informed about the latest trends in automation and quality assurance. And the latest guide from the Test Guild offers several key conferences happening in 2025 featuring both in person and online events. This list is based on real experience, my own and that of real world testers I speak with every day on my podcast. So if you're looking to up your game in 2025, definitely check out that list down below. Are you looking to use open source with your Playwright and Selenium test? If so, this next post is for you. So this post by Jason Arbon, one of the main experts in software testing and artificial intelligence, continues to make notable inroads, particularly enhancing test automation frameworks like Playwright and Selenium and with AI. And his latest blog post dives into how AI driven tools are being integrated to optimize testing processes, reduce manual efforts and increase efficiency. And these advancements are primarily aimed to improve test coverage, mitigate flaky tests and accelerate the testing cycle. Andy goes over quickly how you can get started as well, so I know key players in the space are leveraging machine learning algorithms to analyze test data and predict potential failures, leading to more robust testing practices. Additionally, these AI enhancements help promote smarter decision making in test read management and prioritizing critical test scenarios. And this integration reflects a significant shift towards using AI to address long standing challenges faced by testers such as test maintenance and execution time. And this next article I found on LinkedIn by Gaurav Singh, who recently posted how his engineering team at CRED have unveiled their approach to testing GRPC services and it offers valuable insights into creating scalable and effective testing methodologies. This blog post outlines the adoption of a tailored test automation framework crucial for ensuring the high quality microservices in a fast paced development environment. Credit's methodology Prioritizes efficient test case design and execution, leveraging both unit tests and integration tests to validate service functionality and performance. This strategy incorporates extensive use of automation tools and continuous integration pipelines, ensuring rapid feedback and increased test coverage. The team emphasizes the critical use of performance tests which integrate into the testing process to simulate real world usage scenarios and identify potential bottlenecks. And this approach not only enhances the reliability of GRP services, but also streamlines the testing process, allowing for consistent and thorough quality insurance CRED demonstrates a robust framework adaptable to various service requirements in the software industry. I love real world examples of how other companies are doing certain testing processes and methodologies, and this one you definitely should check out as well. I also just noticed that Test Results IO has just published an encyclopical examining current trends in test automation. This report delves into the shifting priorities for automation tools, emphasizing seamless integration with CICD pipelines and enhanced support for AI driven testing strategies. It highlights the growing number of organizations that are prioritizing end to end test capabilities alongside traditional unit and integration tests to ensure comprehensive software quality. Also, the report discusses the importance of speed and scalability in automation frameworks, pointing to investments in cloud based testing solutions as a notable trend among leading enterprises and software testers should focus on adopting automation tools that offered robust integration with CICD workflows with AI advancements, which I think really underscore the necessity for comprehensive scalable testing solutions in fast paced development environments. This sounds like you definitely check it out down below, right? Next up is another article I found on LinkedIn by Michael, who posted to a new development in Playwright that they just introduced a feature, the Fix with AI button within its HTML reports. What is it? Let's check it out. So this blog post by Vitaly goes over that this development allows testers to leverage AI powered suggestions to automatically resolve certain types of testing failures which help streamline the debugging process and potentially reduce manual interventions significantly. And it goes over step by step how to implement it, how it's used with some code examples. So this is a growing trend with not just Playwright but many other tools integrating AI into test reporting, which I think really helps reflect a growing trend and helping you debug issues as fast as possible. Not replacing you, but augmenting you leveraging these different AI solutions. And speaking of Playwright, another article that caught my attention is by Kailash who explores how to mock the API data requests and responses using Playwright. So the focus of this post is on enhancing tests by utilizing Playwright's capabilities to simulate API interactions, allowing testers to verify application behavior in isolated conditions without relying on live APIs. And Kalesh provides a detailed guide on setting up mock data and Playwright, highlighting the advantages of decoupling front end tests with backend dependencies and this approach aims to minimize test flakiness and improve overall reliability in software test testing environments. So definitely a must read if you want to know how to use Playwright's mock API feature to streamline your test automation process to reduce dependencies and enhance your test stability. Also, I highly recommend you check out his new book on Playwright as well that will help you with full end to end testing using Playwright. Alright, so if you read my blog post on automation trends to follow in 2025 one of them was about agentic AI, so I've been seeing a lot more about this already this year. And one of them is and this next one is all about an AI powered automation tool, the Agentic Browser, which is open source for browser interaction task so Akash has announced the open sourcing of the Agentic Browser, which is an AI powered agent designed to automate web interactions through natural language commands. And this tool, now available on GitHub, enables users to perform a range of tasks such as form filling, web scraping, content retrieval and e commerce product searches with ease. The Agentic Browser offers scalability and flexibility supporting any large language model to execute complex web navigation workflows and this release aims to streamline business operations by automating repetitive online tasks, making it valuable for software testers, researchers and analysts looking to enhance some of the activities they do day to day. So if you're a software tester, you may want to play around with these type of agentic solutions coming out to see if you can leverage it to automate your routine. Web based testing activities help you with your day to day activities to hopefully reduce some of the minimal efforts you put into when you're doing different activities in the software development lifecycle. All right, next up in security news, Microsoft has just unveiled a detailed framework aimed at securing generative AI systems. Drawing on insights gained from red teaming over 100 generative AI products, this framework seeks to improve the resilience and security of AI driven systems by leveraging real world testing methodologies commonly used in software security, and this framework emphasizes the importance of understanding attack vectors specific to AI models such as data poisoning and adversarial inputs. Microsoft approach involves rigorous testing under simulated attack additions to identify vulnerabilities before AI products are deployed. If you're a tester and you haven't gotten the security game. I think this is a must have skill to help your teams, so definitely you should read more about using the link down below. And last up in Security News Suite Security has just released a new detection engine powered by large language models. Reportedly brings cloud detection noise down to an unprecedented 0.04%. This development aims to enhance the precision of threat detection and cloud environments, addressing a critical challenge faced by cybersecurity teams globally. The company asserts that the engine utilizes advanced machine learning techniques to filter and accurately identify threats, significantly reducing the false positives that typically plague cloud detection processes. And this reduction in noise is expected to streamline the workflows for a ton of security professionals, allowing them to focus on genuine threats and improve response times. So if you're doing anything with cloud environments and you want to make them more secure and manageable, this is one solution you might want to check out. All right, for links of everything value we covered in this news episode, head on over to those links in the comments down below. So that's it for this episode of the Test Skill 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.