From Manual QA to Automation Engineer Using AI with Tal Barmeir and Sapnesh Naik

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Hey, I want to share with you one of our most popular Test Guild webinars from a few months ago called From Manual QA to Automation Engineer Using AI and leading it is experts Tal and Sabnesh of Blink IO, who are also the Platinum sponsor of this year's Automation Guild, where they'll also have a session on AI and automation. But before we get into it, if you have big automation testing goals for 2025 but you're not sure how to achieve them without creating hard to maintain automation, well, learn what works from the brightest minds in software testing by attending Automation Guild 2025 online, which is only one week away February 10th to the 15th. That's where you'll learn how to apply AI to your work, the latest in functional and non functional test automation and strategies you could put into place right away. Don't miss out. Secure your future by getting your ticket now by heading over to test skill.com or using the link down below and hope to see you there. All right, in this session we're going to go over how the role of QA is changing in the context of software development and what that means to your career. Also, you'll discover key skills needed to transition from manual QA to automation, including an understanding of AI tools, and you'll also get practical strategies for leveraging AI to improve test creation, management and execution. Also, because this is both audio and video, I left out the demo part. But if you want the demo, just use the link down below and that'll take you to the full video where you can actually watch the demo in action and how you can apply AI to your test strategy as well.

Excellent. We're excited to be here and thank you everyone for joining us for this session. We will really try and take you, zoom you forward into the next generation world of testing where manual testers can actually very quickly become automation engineers using Gen AI. So one of the big messages or news from Gen AI is that actually a lot of capabilities that in the past you needed to bridge by learning, you know, programming language and other things can now actually be bridged using Gen AI. So Ganesh is exactly, you know, the type of person that firsthand experience what does it mean to actually move from being a manual tester to a test automation coder and with that you know he will be able to share from his experience what does it mean when you can leverage today gen AI engines of any sort and kind and actually become a test automation engineer without needing to learn that code. I myself have about 20 plus years experience in the testing industry. Specifically in my last role I co founded and was CEO of of experiences acquired by renamed Digital AI. We did a test automation platform for mobile and web for digital apps. So I've seen firsthand what does it mean, you know, the difference between test automation and manual testing. The big benefits as well as the big challenges of finding people that are good test automation coders. And now again it's really exciting to see that we can actually bring forward manual testers to that in lightspeed. So really the topic of today is how gen opens a fast track career path for testers and we'll be touching on on two. Two I'd say career paths that testers might have boosted with the help of gen AI. The first one is actually a manual tester that wants to become a test automation engineer and generate code. And we'll show you how the capabilities of Gen AI enable you to use the skill set that you have already today as a manual tester and actually generate or deliver test automation code. So you would still be keeping doing and using your manual testing capabilities, but the result of your work instead of just being a one time manual test would be test automation code that can run continuously and all of that without needing to know any code. The second route, which we're not actually going to explore during this webinar, but it's quite interesting as well, is a test automation engineer can now manage a team of AI test engineers and we'll see how you can significantly, if you are already a test automation coder, how you can significantly multiply the productivity by actually managing a set of test automation coders that are powered by AI. So as I mentioned in this webinar, we're covering only the very first career path enhancement manual to test automation engineer. So let's speak a little bit about how does it work today? Okay. Before generative AI was came into the picture, what did it mean that somebody wanted to actually move from being a manual tester to becoming a test automation engineer. So if we look in terms of what are the skills that a manual tester already has. So a manual tester has the ability, the analytical skills, right? So he has the ability to understand what the requirements are and overlay them on the tested application or website. You have the test design expertise to Understand how to define or what type of test scenarios should actually be brought to the picture in order to cover the full capabilities of the new feature or existing features. And finally, it has application knowledge, so we understand the full ecosystem of the application, what is its purpose, who is using it, what is the context in which it is used. So all of this application knowledge, but what does it take for a manual tester today, if he wishes to, to become a test automation engineer? So first of all, you need basics of programming, you need to know Python, you need to know JavaScript. In addition to that, you need to know test framework, right? So at the end of the day, if you want to be a good test automation engineer, you need to build something that scales, something that can reuse methods that have already been implemented and so forth. And for that you need to do a very big investment. You need to understand, know how to operate and manipulate test frameworks such as Playwright or Selenium or any one of those, as well as how to structure the test automation in a way that is scalable, such as using bdd, Cucumber, JS and so forth. So this is what is required today. And the question is, you know, many people that are manual testers find themselves, you know, year after year saying I would like to do this jump. However, life is stronger than many of these aspirations to learn this big gap in coding and test framework. Obviously many times is put aside and year after year passes and you keep on being a manual tester. So now the question is how can Gen AI change that? How can it actually boost your career forward? So with Gen AI you don't need any additional skills required to deliver the productivity and result of a test automation coder. So I'm repeating that with Gen AI you don't need to know anything more than you know today as a manual tester and the results of your work would be the equivalent of what a test automation coder would do. So how does that miracle actually happen? If you perform your test manually, Genai will be able to generate for you two critical things that a test automation coder today would do. The first one is to articulate or build the description of the test, of the test itself in a business level description in BDD Gherkin type of format. Now this is something that you could easily read out and it seems like regular English, but the ability to actually structure it in such way sometimes requires some expertise and training. This is not a huge gap, but it is a gap. And this is something that Genai can just from the fact that you click through a manual test flow can actually understand what was the flow. You just click through the application or website and translate that backwards into a test description that is written on the business logic level. The second thing that Genai can do for you is the biggest gap between manual testers and test automation coders. At the end of the day, a manual tester generates a yes or no result per a single execution of a test flow. A test automation coder produces code that repeatedly runs that test and provides a yes no result that is ongoing and continuously is executed. So the gen AI can take that same flow that you would manually click through, exactly like you'd perform a manual test and create code that would perform that exact type of manual scenario you just clicked through. So this is something which is super powerful. You're clicking through a manual test, but the result that is happening in the background without you even being aware of it is a full blown project. It can be in playwright, can be in other languages, doesn't really matter. It actually codes for you the flow that you manually performed. So now that we understand what Genai can do and how it can actually take a manual tester to deliver work equivalent to a test automation coder, let's look a bit more in detail on the AI engines and understand what is it? What are those different capabilities that they bring to the table? And what I tried to do here is actually cut this into five different main activities that a test automation coder would need to do and see what is the portion that AI can actually do for you. So the first one is the requirement analysis and test planning to define the test scenarios themselves. So a manual tester would usually either do that in plain English, some sort of free English description. How do you actually translate that into a more structured VDD description description? So this is something that AI can do. It can understand a plain free English description, for example of a. Let's say you have a description that says, you know, hey John, you're the manual tester, please create a new opportunity in Salesforce and go then to check that it actually appeared in the pipeline. So that translated into a more structured BDD way a girl can follow the given when, then that is something that you don't anymore need to learn how to do or what are the basics of it. Geni would provide you that directly from the free English description or just from the flow you're manually clicking through like you've done always the second thing a test automation coder needs to do and a manual tester doesn't necessarily know how to is to set up the test automation project. This is actually setting up a code environment which a manual tester doesn't necessarily know how to do. So AI today, Gen AI can actually set that project for you. How can it do? It learns just through the fact that you click through a certain flow, what is the application you're testing. It sets up all the application environment definitions directly into the test automation framework. It knows to parameterize all sorts of fields that you click through. So if you created, let's say, a new opportunity in a salesforce and you filled in all sorts of fields as you went through the process, it would know to parameterize that. Okay, all these things are things that a manual tester does not know how to do. It just, you would just click through the flow, enter the data, but you would not know how to generalize it into a code base parameterization project. And all of that is actually done for you today by Gen AI. The next thing gen can do for you is write the code itself. So, okay, the project has been set, but now you actually want to describe each one of the different locators, the objects that you're clicking through, entering, and so forth. And Gen today is smart enough to actually identify the locators, describe them in a general way, so that the code of that is being generated is stable and scalable. The next thing AI can do for you is once you've actually created this test automation project and it started executing, it can analyze for you why certain tests failed. So one of the big headaches for any testers, including, you know, manual testers that are often asked to look at results, failed results that were executed overnight, is to understand why tests failed. And this is something that is very time consuming and actually holds back the whole team. Because until you identify why tests failed, you don't know to whom to direct the investigation. Is it the R and D because it's a bug? Is it the test automation because the code has been broken due to updates in the ui? Or is it the DevOps because the environment went down? AI can actually do that for you. So if you're now a manual tester, you know, tasked with analyzing all the failed results and trying to understand why that is a task that now gen AI can do for you. And last but not least, all of us know that the biggest task for test automation coders is not just the initial creation of the test automation code, but actually the maintenance of it against all sorts of UI changes that the application or website undergo as the application lifecycle exists for that, you know, you might ask as a manual tester, what does all of this help me if at the end of the day when a test automation test fails because there is need to update the test automation code to UI changes, I don't know how to update that code. I didn't write it to start with. So again here the big message is that Genai can autonomously update the code against the updated UI for you. You might ask how do I know that it updated it correctly? So it provides you an explanation of what it updated and for what changes in the ui. So you actually have as a manual tester the control to approve or disapprove any code changes without ever looking at code just by looking at the screenshot that the gen explains to you with what was the reason it changed the code. So up to here is really, you know, what are those capabilities that a gen AI engine brings to the table today? The next thing that is important to understand is that today as a manual tester you're limited many times to the languages that you speak and as a test automation coder, you are limited many times to knowing to code only on mobile or web and not necessarily know how to code across all platforms. Gen AI has this ability. It's fully multilingual and it's able to create test automation code for you even though the manual flow that you are executing based on which it creates the test automation code has been executed only against a mobile or only against a web application. Now this is a big thing because it means that for a manual tester, click through a certain flow, let's say on a web application, and then the gen AI knows to create not only the code for the web application, but also for a mobile application, whether it's iOS or Android. And if you ask yourself how can this be, the answer is that gen AI actually creates a full model of what the business logic of the flow you just created in the manual task represent and implement that against an application, whether it's a web, whether It's a mobile, iOS or Android. It doesn't really care. By the way, very similar to the way the brains of a human manual tester would work. If I gave you a test and I gave you a mobile application or a web application, you'd figure out how to actually perform the manual test flow. Genai has enough intelligence to figure out from one flow that is performed against say a web application how to perform the same business logic against a mobile application and create the code for that. So think about it now, from A manual tester performing a one time manual test, you become a test automation coder. Creating test automation code on multilingual websites and on multi platforms. The value that you bring as an employee to your organization has been multiplied in an unimaginable way. And that can happen today with the help of gen AI without any need to learn code or to learn different languages or to learn different platforms platforms. I'd switch over here to SAPNES to speak a little bit from your experience, you know, moving from a manual tester to a test automation coder, how can actually gen AI help you and what type of capabilities somebody can actually learn to make this path best?

Thank you Tali. So what you're seeing on the screen here is like general path for engineers to become an automation test engineer from a manual test engineer, right? So you already have some skills as a manual test engineer. You already have, you know, your application. If we give you a feature description, you know, to generate different kinds of test cases like note it down and then test a feature like in a positive happy path or negative path, you can already understand the edge cases as well. And since you are doing this day in and day out manually, you also have a very keen eye for detail. So this still applies to automation testing as well. So if you can bring all of this and then on top of this learn a few skills and then translate this into code automation code. You have already written excellent scripts, right? Excellent test scripts. So what new you need to learn is basics of some simple programming languages like JavaScript. So JavaScript can be used to do very advanced stuff as well. But the learning path for that learning curve is very easy. We'll be able to learn that in like a week or two. If you start with basics and then learn Playwright. Playwright or Selenium, anything. But I would suggest Playwright today and then learn Cucumber JS as well. So Playwright will allow you to basically automate whatever happens in the browser, right? Like clicking the actions, navigating to a page, filling in something, all of that. And Cucumber JS will allow you to like organize your test cases, define test suits like regression or feature based and then run multiple test cases in a suit and then get reports and all of that. Yeah, and GitHub is also, you might have already heard GitHub. It goes without saying you need to know version control as well. But once you learn these basics and start exploring AI tools, you can see that even with like learning 20% of it, with the help of AI, you are able to reach 80% of the test automation. So that's what it will help you do, right? You learn the basics and then you start exploring AI tools and that will take you to the full 100% and you will be able to automate your test cases.

Okay, thank you for that Supnesh. And how can you actually use AI to get ahead in qa? You know, you're experimenting a lot with this. What are your suggestions or you know, ideas or you know, manual testers looking at AI and saying, how can it help in my career?

Yeah, yeah. So manual testers didn't have this like a year or two ago. So now we have chat GPT as you all know. So I would say use Chart GPT extensively to like arrive at a faster research, faster solution, faster research, faster results. So that will help you a lot. That's my personal experience also. And whatever AI tools you're exploring, right, make sure you document them somewhere in like your personal blog or else your GitHub repository. So make sure you have a record of that so that you can go back and revise it. And let's say you are giving any interviews as an engineer, test automation engineer, you can also talk about the AI tools that you explored. Right? Because we are seeing that a lot of companies are exploring AI based testing now. And if you are able to talk about what AI can do in the testing industry, like how it can generate code or generate test ideas and improve coverage and automatic recovery, all of these, if you're able to talk about it in your interviews, that will be a huge plus for you in clearing interviews too.

Yeah. Okay. Well guys, we're going to demo on our tool on Blink IO. But the insights and capabilities we're demoing here are just a result of the general capabilities of generative AI today. So you have to understand that this is general capabilities that are available today and you'll probably see them in open source tools and all sorts of different offerings in the market. So we'll try to keep this as general as possible, focusing on the general generative AI capabilities. But again, feel free to plug in questions that you'd like to, to, you know, to ask about this. And obviously if you'd like to specifically try our tool, we would love you to come to our website and, and check it out. Okay, so I'm handing over here to Supnesh. Yeah, we go for a live chat of the different gen AI capabilities.

Thank you talent subnesh for your automation awesomeness, for links, for everything of value we covered in this episode. Head on over to test guild.com forward/A533 and while there, make sure to register for Automation Guild. You have one week. If you miss it, you can also get the full replays using the link down below. And once again hope to see you there. So that's it for this episode of the Test Guild Automation Podcast. I'm Joe. My mission is help you succeed in creating end to end full stack automation awesomeness. As always, test everything and keep the good. Cheers. Hey, thank you for tuning in. It's incredible to connect with close to 400,000 followers across all our platforms and over 40,000 email subscribers who are at the forefront of automation testing and DevOps. If you haven't yet, join our vibrant community@testguild.com where you become part of our elite circle driving innovation in software testing and automation. And if you're a tool provider or have a service looking to empower our guild with solutions that elevate skills and tackle real world challenges, we're excited to collaborate. Visit test guild.info to explore how we can create transformative experiences together. Let's push the boundaries of what we can achieve.

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