Appium Interceptor ,Automation Trends, Playwright and More

What are some of the top Test Automation trends in 2024? How do you do contract testing using Playwright? Have you seen the video on quickly learning to build, train, test, and deploy machine learning models? Find out in this LinkedIn Newsletter edition of The Test Guild News Show for the week of January 7th. So grab your favorite cup of coffee or tea, and let's do this.

Automation Guild 2024

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Puppeteer + WebBiDi

First up, we have Puppeteer, where I saw a post by Michael Hablich that announced a new capability on how they've extended its capabilities to Firefox utilizing the new WebDriver BiDi protocol. WebDriver BiDi is a fusion of the best features from WebDriver Classic and Chrome Dev Tools Protocol, allowing bidirectional communication for more efficient and capable browser automation. This development promises granular control and an efficient automation experience. Progress has been made in Puppeteer's implementation, enabling features like logging network events and form submissions through WebDriver BiDi. They also say Puppeteer's integration with WebDriver BiDi is still in progress. While features like Cookie Access and Network Request Interception are still under standardization, the current state demonstrates the protocol's growing functionality and reliability. If you still need to check out Puppeteer or WebDriver BiDi, it's a great way to learn them together.

Test Data Game

Want to know the four difficulty levels of creating test data? I found a link to an article that will help you with this.

Ivan Karaman's article likens data generation for testing to difficulties setting in a video game. And it's a fun way to learn more about different test data approaches you could be using. So, one level is the Hell level, which equates to manual data entry. It's deemed the most challenging and also the least recommended. Suitable only for rapid one-off situations with restricted access. Also, the nightmare level involves automating data entry through UI, which is better than manual input, but it could be faster and more reliable. And I like number three, which should be the normal mode; he calls it the normal level. And that's seen as a more balanced approach, which advocates using APIs for data generation. This method is significantly faster and more effective than the UI approach, bypassing the need for a heavy UI framework. And for those unfamiliar with coding tools like Postman, it can also facilitate this process. And lastly, he goes over the easy level, suggesting direct database inserts. However, this approach comes with caveats like potential complexities in the database structure and access restrictions. But he advises using the API method if these issues arise when you try to do the easy level.

API contract testing using Playwright

The following resource by Andrey Enin is all about API contract testing using Playwright. This approach by Andrey goes over a novel way to ensure that front-end applications interact correctly with third-party APIs, so contract testing, which is continually used to verify interactions between microservices, is now being applied to client-server architectures. However, the front end is a consumer or provider of various APIs. This method is helpful with third-party APIs that cannot be directly accessed during tests. Playwright comes into play by allowing the simulation of API interactions at the network level. I liked how this article highlights a practical example of using a cryptocurrency app that demonstrates how Playwright can interact and intercept the test API requests made by the front end. This process involves checking the request contract against predefined schemas, ensuring the front end makes specific requests in the correct format. This approach to contract testing on the front end not only streamlines the testing process but also provides a high degree of accuracy and reliability in the software development of your application. So, this is another cool feature of how you can leverage Playwright for other types of testing. You can find that link in the comment below.

Top Automation Trends for 2024

Also, as it is the first News Show of the new year, I want to share with you my annual top Automation Trends for 2024. I just released this. It covers the top 12 automation testing trends you need to know in the new year. This year, one of the key takeaways is the increased integration of AI machine learning, its automation testing promising more effective, efficient, and accurate results. Also, a significant shift was seen in testing practices, focusing on catching bugs earlier in the development cycle, the rise of testing directly in production environments, and how to handle real-world users at scale. This article goes over how to do that and what my top trends are that you need to know, and I'm curious to know if you're seeing the same thing. I'd love to know your thoughts after you read this article. What did I miss? What may I have been spot on? Or if something needs to be corrected in the comments below.

Building, testing, training, and deploying a machine-learning model

So, seeing how AI will grow more in 2024, it's not a silver bullet. But as a tester, you need to know more about it. You need to understand how you can build it. So, I found a great resource that teaches you how to do this. This is by Geosley Andrades, who just dropped a new video about building, testing, training, and deploying a machine-learning model using an Azure free account. So, the video goes over creating a workspace in Azure Machine Learning Studio and how to upload and validate the data set for training. He goes over setting up a compute cluster for machine learning. There's an example of automating the machine learning algorithm using Azure. As I mentioned, it is really critical. It goes over how to test and deploy the machine-learning model as a real-time endpoint.

Appium interceptor plugin

I also just learned of a new Appium plugin you should know about. This is by Sai Krishna, and it's called the Appium interceptor plugin. And Sai has just released this in its first beta version. This is an excellent tool because it's designed to simplify mobile automation by introducing the ability to intercept and mock API responses. So utilizing the mitmproxy, a well-known two in the field of network monitoring, the plugin operates by starting a proxy server for each Appium session, and this server then redirects all network traffic from the device through the proxy, allowing for detailed monitoring and manipulation of API responses. Cool technique and the powerful plugin is also user-friendly with mocking capabilities that are disabled by default to ensure ease of use. You can also enable this feature by setting the Appium intercept capability to true when interacting with a new Appium session. This flexibility makes the tool adaptable to a wide range of testing scenarios. All your developers and testers in the mobile app space are encouraged to try this out. Also, Sai seeks your feedback in the community to redefine and enhance this capability further. Give it a try, and let Sai know what your thoughts are.

Scale Performance with the The Actor Model

So, I love looking for real-world stories on how people succeed in making their applications functional, correct, and performant. So here's an article that goes over how PayPal did this. Have you heard the remarkable story of PayPal's system design evolution? Starting in 1998 in California as a security software team for hand-held devices, PayPal pivoted to an online payment service due to the failure of its initial business model. Facing explosive growth, PayPal quickly reached a million transactions per day but soon encountered the limitations of Moore's Law as single-threaded performance gains slowed down. The traditional approach of scaling up with newer hardware became inadequate, leading PayPal to adopt horizontal scaling with over 1000 virtual machines. However, this solution introduced new challenges, such as increased latency, costly network maintenance, complex infrastructure, and inefficient CPU usage.

In a transformative move, PayPal turned to the Actor Model based on the Akka framework, a conceptual concurrent computation model where actors are the fundamental units of computation. This model enabled PayPal to handle a billion daily transactions with just eight virtual machines. The Actor Model is characterized by its lightweight actors that operate asynchronously and are isolated from each other, communicating solely through messages.

This approach allowed PayPal to achieve extreme scalability and efficient resource utilization, overcoming the hurdles of horizontal scaling and fully leveraging its hardware. The Actor Model's success at PayPal underscores the importance of innovative system design in handling massive scale in the digital payment industry.

One of the trends on my top 12 trends for 2024 was observability. This next article goes over open telemetry and observability in a little more detail.

The Year of Observability

So last year, I talked to many people, and many people spoke about Observability and Open Telemetry, how it made a lot of different achievements and stable releases of three-course signals tracing metrics and logging. This development represents a big step in realizing Open telemetry's vision as a standard framework for collecting observability data.

This article highlights critical highlights from the year, including the widespread adoption of open telemetry specifications, metrics integration of distributed tracing, and leading load testing tools like Grafana, K6, and Artillery. These achievements have enabled more detailed analysis and performance optimizations during load testing, which is critical and also highlights the growing trend towards expanding the use of observability tools into development and testing phases, which is essential if you haven't checked out observability as a tester, 2024 is a year that you need to learn more for sure.

Next Step

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