Revolutionizing JS Testing with AI: Unmasking the Future of Quality Assurance

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"Revolutionizing JS Testing with AI: Unmasking the Future of Quality Assurance" is a forward-thinking talk that delves into the transformative power of AI in JavaScript testing. The presentation offers an enlightening exploration of AI Testing principles, practical applications, and future potential. By featuring AI-driven tools like Testim, ReTest, Datadog, and Applitools, this talk brings theory to life, demonstrating how AI can automate test case generation, optimize anomaly detection, and streamline visual regression testing. Attendees will also gain insights into the anticipated advancements in AI Testing for JavaScript. The talk concludes with a lively Q&A, inviting everyone to delve deeper into the world of AI and JavaScript testing. Be prepared to reimagine your QA process with AI!

Renaldi Gondosubroto
Renaldi Gondosubroto
20 min
11 Dec, 2023

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  • Swati Gajjar
    Swati Gajjar
    Hello, at end of your presentation you are showing some demo of AI powered code less testing tool, can you provide the name of that tool?

Video Summary and Transcription

AI testing with generative AI is revolutionizing JS testing by automating test creation and improving software test processes. Key technologies like natural language processing and neural networks, as well as quality data, play a crucial role in AI testing. The benefits of AI testing include speed, efficiency, adaptability, bug detection, and limitless potential. Generating JavaScript tests can be tailored to different tools like Selenium, and there are popular tools available for automating test automation. AI tools like Datadog, RecheckWeb, and Applitools Eyes offer powerful capabilities for anomaly detection, visual regression testing, and code list testing. The horizon for AI in testing continues to expand with evolving capabilities, and understanding AI's role in testing revolution and machine learning is crucial for practical application and continuous learning.

Available in Español

1. Revolutionizing JS Testing with AI

Short description:

Hello everyone! My name is Rinaldi, and today I will be delivering a talk on revolutionizing JS testing with AI. AI, particularly generative AI, has been making big strides with changing the landscape of programming and testing. It has opened up opportunities for improvement within quality assurance. We will explore the growing trend of generative AI within testing, its potential to automate test creation, and how it revolutionizes the landscape. The role of machine learning in AI testing is to improve software test processes, prevent human error, and automate error detection based on history.

Hello everyone! My name is Rinaldi, and today I will be delivering a talk on revolutionizing JS testing with AI. Unmasking the future of quality assurance. So as you probably already know by now, AI, particularly generative AI, has been making big strides with changing the landscape of different kinds of programming. Javascript is just one of them. And not just the programming aspect of itself, but also the testing element as well. And that has been able to open up a lot of opportunities for improvement within quality assurance. And hence, that's where we're going to delve into this topic today.

So without further ado, let's get straight into it. So I'd just like to briefly introduce myself. So I'm a software engineer for Seek. I am also a holder of all 13 certifications from AWS. I'm also a subject matter expert for the Solutions AWS Architect Professional and AWS Data Analytics Specialist Certification. I'm an international speaker at over 30 events and conferences. I also enjoy all things AWS, open source, testing and virtual reality.

So diving into this topic directly, what is the main meat that we want to get into today? Well, really it's all about being able to understand the growing trend of generative AI within testing, because we've seen a bigger trend of how it's currently being conducted within the realm of testing. And nowadays, it's not only that you can just automate and create new text or generate new stories with generative AI, but now you can actually create code with generative AI, create tests with generative AI for your code. So it just has so much potential within what you can do with it. And as mentioned before, this leads to a lot of new areas such as codeless creation of test cases. And of course that then leads to the potential of opening test creation to anyone. So it's not only those who are very well versed within test creation that can do this, but normal devs or even non-technical people can even start looking into this and help out with the development process too of tests. So in general, it's just revolutionizing the landscape in a really big way.

What is the role of machine learning within AI testing? Well, firstly, we're using AI to be able to improve software test processes. It's becoming an assistant for us to be able to work with, to be able to create us a template to be able to build on. And aside from that, it helps us to ensure that what we are doing is right. So one of the things that is very common in test case creation is the occurrence of human error. Introducing AI to the mix, it can help us to prevent that from happening and redirect us instead to be more focused on how we can make better tests and how we can make more error proof tests. So that is the power of generative AI. And we want to be able to also automate error detection based on history. That's one of the things that it has been able to do for us too, because what we can do is that we can create an automated process where error handling and error checking is a normal thing, so that AI can immediately just check based on the history. Maybe there could be potential errors here and accordingly just provide and provide better suggestions based on that.

2. Key Technologies and Data in AI Testing

Short description:

Aside from redefining quality assurance, AI testing involves key technologies like natural language processing, predictive analytics, and neural networks. The role of data is crucial as feeding quality data determines AI's performance. Fine-tuning solutions requires sufficient data.

Aside from that, it is also redefining how we are able to perform quality assurance, as mentioned before, we can also integrate it as part of our pipeline and hence build based on that to be able to ensure that the quality that we have in each stage is assured because of the checks that the AI does.

So what are the key technologies that are involved in this? To name a few, some of them include natural language processing, predictive analytics, and neural networks. Natural language processing, for example, in this particular case scenario is a very important thing because it really determines how we are processing the text that we put through. And that's why problem engineering is a very big thing within AI because we want to make sure that we are actually telling it the right instructions instead of making it vague. Well, we're going to cover it a bit later as well.

Aside from that, there is a very big role that data plays in this because feeding the AI with quality data really determines how well it's going to perform. We have seen a lot of different providers such as chat.gtbt or Amazon's bedrock models perform whether it be good or bad based on a number of parameters that they are fed based on the data that has been used to train them. So it really affects this and it's important to understand that this affects it as well. So if you, for example, decide to look into fine-tuning your solutions, that's definitely a big consideration because you want to make sure that you are fine-tuning it based on enough data and not just partial data.

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