Dinakar
All right, great. Looks like we have a full house today. So, hello everyone and welcome to Pcloudy's webinar on how modern QA teams QE teams are testing web, mobile, and APIs from one AI platform.
Dinakar
My name is Dinakar and I'll be the host for today. Before we get started, I would like to go over a few housekeeping rules. You may have noticed that your mic is currently muted. And if you have any questions or clarifications, you can use the Q&A option available at the bottom of the screen. We will answer your questions as part of the Q&A section towards the end of the webinar. And also, please note that this webinar is being recorded and we will send the link of the recording to watch again or share with your colleagues later.
Dinakar
With that said, let me introduce the speaker for today, Jessica Selvin. Jessica is a lead solution engineer at Pcloudy. She has around 5-plus years of experience in the IT industry. And prior to her role here at Pcloudy, she has worked with LTIMindtree as a pre-sales consultant in automation solution design.
Dinakar
And she's done this for various industries through LTI. And she's She has a keen interest in data analytics, machine learning, and various AI tools. So, without further ado, let me hand it to our speaker, Jessica Selvin. Over to you.
Jessica Selvin
Thank you. Thanks, Dinakar, for the introduction and hello, everyone. So, let me get started by first introducing our topic for the day. yeah, I hope my screen is visible.
Jessica Selvin
Dinakar, can you confirm?
Dinakar
Yes.
Jessica Selvin
Yeah, thank you. So, as Dinakar has already highlighted, we've been looking into how modern QE team can really optimize their testing through be it for web, mobile, or API all in one AI-powered platform, all right, eliminating tool sprawl, you're able to ship your builds faster, right? And scale quality without, scaling your head count, right? So, that is the core of what we'll be looking for and looking at today. going through the agenda, we'll first look into how AI-powered testing really does change things. right? And how complex workflows can be done without a single line of code. right? And then, we'll also be looking into unified testing platform, and how a unified platform can really help optimize your testing.
Jessica Selvin
How you can eliminate tool sprawls and end-to-end regressions with one trigger, and finally scaling your quality. So, firstly, looking at the core problem, right? lot of QA teams, and you might find this relatable into your own day-to-day testing, right? is very fragmented. There are several tools working in silos, right? That don't communicate with each other, and the teams are also working in silos.
Jessica Selvin
Right? So, they're juggling four to five different tools, managing their testing stack, and the problem isn't just the number of tools. it's the fragmentation that comes with it, right? So, the teams end up using one solution for API, another for mobile, and then a separate framework for web. Maybe you're running those locally, and then you have a couple of mobile devices, and then maybe you have another tool for APIs. right? And so, there's a lot of tool fragmentation with no visibility. right?
Jessica Selvin
So, often when it comes to APIs, the teams would be relying on a dedicated API testing routine just to validate the back-end services and integrations. then for mobile, maybe you'll just have a couple of devices, right? Or for devices, and maybe most of the testing goes on emulators where you can't really get a real world experience on emulators and simulators. right, and this becomes disconnected from your workflow.
Jessica Selvin
And then web application, so there's another automation framework in place and maybe you're running that locally, but you're not able to achieve true cross-browser testing because you don't have all the versions in place. And then finally coming to test data management, right? a lot of that is also still manually heavy, right? Teams are maintaining spreadsheets, they're maintaining the test cases on an Excel, right? And individually, right? And the tools may not work together well.
Jessica Selvin
So right, this creates operational complexity and then questions come up like who's in charge of this tool, right? Or whether that the latest data was optimized across all these different tools. So all these questions and troubleshooting comes with managing your tool stack, troubleshooting the integrations, and maintaining these frameworks. Right? So with that, the result inevitably becomes slower releases of your applications, right? And especially in today's day and age with AI and developers increasing their speed, right? Testing is not able to keep up. So this would additionally add to slower releases, fragmented visibility, right? Between teams, between tools, there is no single repository of reports, and the QA team is stretched thin, right? So instead of focusing on quality strategy, innovation, right? You're stuck in main, more of the grunt work of maintaining tooling, updating your scripts, managing your environment, right? Taking care of the operational overhead, right? And so what organizations should really move towards is consolidation. right? So with that, let's get into the first segment, which is AI-powered testing, how that will become the core that would drive this entire solution.
Jessica Selvin
All right, and you'll be able to overcome the challenges of traditional automation, right? So, this often requires coding expertise, all right, script maintenance, all right, and especially resources adept in the knowledge of framework and scripting. Right? So, AI brings about smarter test generation and opens up the test automation to your manual QA as well as business analysts, so that you don't have a restricted pool of resources working on your automation scripting. So, yeah, just elaborating further on that, you'll be able to describe your test cases in plain English, all right, you'll be able to go ahead and tell AI what you need to do, and the AI will manage the rest, right? And this fundamentally changes the speed and efficiency of QA teams, all right, so whether it's an authentication workflow or a mobile user journey or an API validation, you will be able to simply describe your scenarios in plain English, all right, and it won't just create happy path tests, it will be able to intelligently generate positive, negative, and edge case flows as well, so that you can ensure deeper coverage and stronger application reliability.
Jessica Selvin
So, what would typically take hours of manual work can happen within hours or minutes, all right? And finally, we have a chained workflow testing, all right, so this is where the platform can become really powerful, where the AI can understand the relationship between steps and it passes data from one stage to another, right, and having managing your test data and all of that. And yeah, coming to assertions and validations, so you'll be able to give your assertions, validations, validating pop-ups, if conditions and everything with just in one platform. All right, so what this and what we've seen it done for our teams who have adopted our AI agent suite, all right, we see a 10 times faster test creation, all right, and automation is no longer restricted only to a fixed user base, right? It's a good especially if you're in an organization and who has low automation maturity, all right, this is a great way for you to get started with automation, all right, and improve your coverage as well. Able to have 100% coverage of multi-test step workflows, all right, and all with zero lines of code and zero scripting knowledge. The next segment is on the unified testing. So, as we had already highlighted on the biggest challenge of orchestration, so instead of triggering separate pipelines, having separate tools, all right, you have one trigger to run everything together, right? And this also gives you better visibility across your QA, your developers and DevOps teams because everything is aligned on the same platform, all right, and there's a single source of truth.
Jessica Selvin
So, unified testing will help organizations move from your siloed automation to more of a connected quality engineering approach making testing faster, smarter and more scalable. So, you'll have more of a one platform, one view, and one trigger to run everything. And that would include, yes, your web testing, right, where you would have a browser infrastructure across different browsers, all right, where you can have easy compatibility with your traditional frameworks like Selenium, Playwright and Appium with our AI runs as well. So, you'll be able to achieve Windows and I mean achieve testing on Windows and Mac machines across different versions and browsers. Similarly, on the mobile testing, a huge variety of iOS and Android devices so that you don't have to rely on maybe those few physical devices that you have in your lab, right? And instead relying on a platform where you're able to have a large number of devices and device variety across OEMs and versions to achieve true compatibility testing.
Jessica Selvin
And the API testing as well, so that you your back-end functionality as well is encompassed in one platform and managing all this with the test data. Right? And then coming to the third section, right? we see it to be of eliminating your tools costs, right? And cutting your test cycle time. right? So, teams get one unified view across all the testing layers, right? So, whether it's validating a UI flow on the web, and then you need to run something on your mobile, and then you also have a couple of API checks to run. All of this will happen in one platform. And the biggest advantage is orchestration, where you can trigger it together.
Jessica Selvin
Right? And view those reports and store it, share it all from one platform. Right? And unified testing would make things much more scalable, right? So, that you are able to quickly scale your automation. You don't need to worry about the operational overhead that we talked about in terms of maintaining different tools. So, how it would practically look like, right? So, assuming say you have your Selenium/Playwright for web, right?
Jessica Selvin
With our Agentic AI suite, you would have web testing built in with no setup because we have our browser infrastructure as well, where we have real Windows and Mac machines, and different browsers like Chrome, Edge, Safari, Firefox across different versions. So, you'll just simply be able to generate your test scripts on those and run it on our platform and get the reports. Right? So, you wouldn't have to maintain, elaborate scripts, right, and have a dedicated pool of resources just to handle this. next coming to maybe you are having Appium or a separate mobile tool, be it locally or having a few handheld devices, or maybe you're having a separate device farm. right? So, again, this would be taken care by our AI Agentic AI, where you can generate your test scripts on real devices and then execute them on real devices and get the results. And typically, maybe you would have a Postman or SoapUI for APIs, right?
Jessica Selvin
And again, API testing also will be handled within, single platform, so you don't need to have a separate, Postman installed, right? And then manual test data spreadsheets, all right? So, you might be managing everything on an Excel. Here, you might you can generate your test cases and store it in projects and folder formats. Of course, you will be able to export it to Excel whenever you want to, but you'll also have a single unified source where you can simply access all your test cases and scenarios. And, you would have for all these different tools, right, like your Appium, Selenium, right? You might have separate CI/CD integrations.
Jessica Selvin
Here, you would have a standard CI/CD trigger for all these, layers. So, that is the shift we're talking about. So, this is the, how, in coming to see how it would actually work in real life. So, this is the kind of tools integration, you would be able to see and the unified testing that we talked about. next coming to how this would help with your regression runs. All right. So, you will be able to trigger all your scripts in one run. All right.
Jessica Selvin
And that means you have better traceability and reduced operational overhead. And you also have better collaboration. All right. So, how it would look like is firstly, maybe you'll do a the your flow of any maybe the developer gives a code comment. And whenever he or she pushes the code, it merges a pull request or a scheduled pipeline run and the process automatically moves into the CI trigger stage. And from there, the platform orchestrates multiple testing layers together, right? So, you would have your API test execute first validate the back-end functionality. And then you would have your web test validate the user interface and browser behavior. And then mobile test ensure that consistency is there even for your mobile application and it works both for Android and iOS. All right. And finally, everything flows into more of centralized reporting and alerts where results are automatically shared with the team, right? And what makes this powerful is the automation and intelligence that is built into the workflow. So, even for the reporting and everything, you would see that there is an AI analysis built in which will give you insights into the logs and analyze and give recommended suggestions.
Jessica Selvin
And you don't need to run or start your test manually. All right. You can schedule it or you can trigger it from your CI/CD. And right. And you'll also be able to support parallel executions. That is something because of our diverse infrastructure. You will be able to run either sequentially All right. Or parallelly, right? Pick your range of devices and browsers. All right. And simply go ahead and run your test suite.
Jessica Selvin
And finally, we have the smart triage of failures using AI. All right. so rather than just showing where which test failed, it helps identify what is the probable root cause, right, reducing your debugging effort for QA and developers. And another major advantage is the instant reporting. So, we have something called progressive reports, where results can automatically be pushed, I mean, seen on your emails. And if you go to our report section, you'll be able to see all of them. Even test cases that are currently in progress will be able to be shown. And you can also log it into your Jira or Slack in case of any bugs.
Jessica Selvin
And the platform also can detect flaky tests, right, and also there's a complete audit trail of all your executions including the text logs, the videos, right, and the logs. So, this approach would fully transform your regression testing from a more of a slow manual activity into a fast, intelligent, and fully automated quality pipeline. So, finally looking into what kind of results we see, with this kind of adoption of our Agentic AI suite, right? So, firstly, what we've noticed when our customers adopted our Agentic AI suite, there's a 60% reduction in QA cycle time, right? From 2-week cycles to under 5 days, right? Four times the faster test creation, right? For an individual and also with more teams working and more resources working on Agentic AI because it doesn't require any framework knowledge, really accelerates the test case creation time.
Jessica Selvin
Fewer production bugs, right, because we have very accurate and reliable AI models that are generating these scripts, right, and no overhead and manual testers being able to into automation results in no extra hires needed. All right, so that is some of the real results we have seen when it comes to adopting agentic AI cross platform for web, mobile, and API. So next, I would like to now take you through the demo itself of our platform and showcase how our agentic AI suite works particularly on the API testing side as well. So just give me a minute to show you that.
Jessica Selvin
Yeah. So coming to our Pcloudy platform. All right, so we are a digital experience testing that's AI powered platform and we're SaaS-based. All right, which means you can access it anywhere, anytime, and once you have signed up and your licenses are allocated, this is how the platform would look like for you.
Jessica Selvin
Where you can access real devices and browsers, so that forms the core of our platform where we're able to support all these functionalities with the core of our infrastructure having real mobile devices and browsers. So you can see the range of Android devices that we have across different OEMs here and even on the iOS side, we ensure to have all the latest makes and models along with the earlier versions so that you can achieve true compatibility testing. Now coming to the AI agent suite that is built on top of this infrastructure, right? yeah, I'll quickly show the browsers as well. So, we ensure to have Windows and Mac machines and all the different Mac OS's across different browsers and browser versions.
Jessica Selvin
So, this forms the core of our solution, on top of which we have built our Agentic AI Suite. So, firstly, we have QGen, which is a test case generation agent. right, we have a more detailed, webinar on this as well. but just to quickly give you a highlight about how that works, right? So, we have you can create your projects and have a folder structure, right? And you can see we've already created, several test cases, right? So, this is how it would look like, the end result. right? So, you would just simply have to create a test case, right? And upload different inputs, right? So, you can either upload an image or you can paste your user story or functionality or you can give your Figma URL or the URL of your application. So, you can give several input types, right? Into the application.
Jessica Selvin
So, I'm just going to maybe upload a screenshot, but you can also upload multiple types of inputs, right? So, you can upload, maybe a screenshot along with a PRD document and, the Figma URL, right? So, you can do multiple, inputs. So, you can see here I have just given the screenshot of an IKEA application. And then I'm going to go ahead and analyze this.
Jessica Selvin
So, you can see right now it's processing the inputs that you've given, right? And generating all the possible test scenarios, right? So, you will be able to, filter out what would be relevant for you, and your use cases, on the basis of which you can shortlist and give it for test case generation. So, you can see here it's generated all the different possibilities of scenarios to validate, for example, the back navigation, the product title, the description accuracy, the price display verification, all these different scenarios are given, and you can just choose which are the relevant scenarios for you. All right? Or if you want, you can refine it multiple times with AI again.
Jessica Selvin
And once you've selected your scenarios, you can go ahead and generate your test cases. So, this will actually give you the end result where you will have the test case steps along with the priority, right? Which you can then maintain in our test case generation as your manual test cases, right? Or exported into an Excel or a CSV, or feed it as an input into our Qpilot agent, where you will be able to automate it and execute it. Right? So, as this is generating, maybe I'll quickly showcase an already completed scenario. So, you can see here, this is how the end result would look like, where you can see the test case steps, all right?
Jessica Selvin
Which can be edited, and you can modify priority, description, or add and remove test cases. And once that's done, you can simply export it into an Excel or a CSV. Right? So, this is on our QGen or our test case generation agent.
Jessica Selvin
Next, coming to our Qpilot agent, where you will be able to actually scale it into automation, right? So, we have our agent here, where you can create multiple projects, your folders, right? So, and you can then go ahead and create your test case scenarios. We support web, iOS, and Android all equally. So, in the cases of web, you'll just simply give the URL.
Jessica Selvin
For iOS, you would give your IPA file and bundle ID. And for Android, you can simply select your APK file and then go ahead and give your package name and your app activity name. And I've given a test case name and I can select which device I want it to generate on. So, I'm just selecting a device and saving and going to play now.
Jessica Selvin
So, here what is happening is firstly, the device that I've selected is going to get launched and the device session will get initiated. And then the APK file that I've selected is going to get installed and then launched onto the device based on the details that we've given of the package and activity name. And finally, the Qpilot model itself is initiated where I can simply give plain English language descriptions and it will convert it into a set of automated steps. Right? We have a separate detailed webinar on this, of course, so you can have a look at that as well.
Jessica Selvin
So, for example, here we have enter the username in username field. Right? So, just plain English language description. right? And right now you can see that the test step is getting parsed. right? And the Qpilot model is trying to understand your intent. going through the entire DOM structure, seeing what is the closest fit, and accordingly doing the action on the application itself. right? So, you can see that it's now entered the username in the username field. And if I click on the drop down here, you can see that it's identified the unique XPath and given me the reasoning as to why it selected that particular element.
Jessica Selvin
So, similarly, I can give my entire test flow. There is no limit on the number of steps you can give. In the interest of time, I'm just giving you a few steps. So, you can see now, the step is again getting passed. It's going through the DOM structure.
Jessica Selvin
All right. So, we have several AI agents working behind the scenes. like an XPath locator agent and a step generation and a step execution agent. Where all of these work hand in hand to ensure that it's first identifying the XPath and then generating the step and then there's a step execution that actually does the performs the execution. And then finally, you have the code generated. So, this will ensure that every step, the XPaths, and the automated step is highly accurate because it's already validated by executing it first. You can also add validation steps, pass your test data. So, in case if you do not want to give it as hardcoded, you can simply give it or pass it as a test case test data. So, here is where you will import your test data where you can simply give it as a JSON format.
Jessica Selvin
We also have options of adding API steps. Right? So, you can simply call your methods, add your API URL, give your authorization, and add it as an API step as well so that you can incorporate your API testing or any API validation that is needed directly into your workflow. Next, coming to once your script is already generated, you can Let me just quickly show you the generated script. So, this is how your full end-to-end script would look like with all your test cases.
Jessica Selvin
If you disable this toggle, you'll be able to see all the information as well like the XPaths. And you can simply go ahead and select run, and select the device of your choice, and click on run test, and it will initiate the execution successfully. Right? So, that is on the mobile web and iOS functional testing aspects.
Jessica Selvin
So, next coming to our final agent, which is our QVerify. Right? So, here is our QVerify API testing agent. So, now Pcloudy can also validate your end-to-end API testing scenarios. Right? So, you can create your projects, folders, and your test cases right here. So, there is no need of having fragmented tools like your Postman to validate your API functionalities. It's all right here in one platform. So, I can just go ahead and create either a new test case.
Jessica Selvin
All right? So, here I already have my test case created. So, this is a simple authentication test case, and then I would get the drive link. And this is to book a device on Pcloudy. So, these are Pcloudy's own APIs, and we are doing our own API testing on our QVerify solution. All right? So, you can see here that we first have the authentication. All right? So, you can give your the your method, your step name, the endpoint URL. All right? And your authorization type.
Jessica Selvin
So, whether it's no authentication or whether it's key based or basic, you can choose the authentication type. And give your username, password, or if there's any token, you can pass that as well. All right? And then you can also define any variables of your choice. You can give specify the number of retries you want, and what is the request timeout. You can also add any particular validation criteria of your choice.
Jessica Selvin
All right? And finally, do the test workflow. All right? So, I can just validate this particular test step. So, this is giving me the authorization token. right? I can So, I can test it as an individual or I can add multiple test steps. I can keep adding multiple API flows and test it all in a single shot. So, for example, if I run this test case, you can see that both my authentication and fetching the files from the drive both have been successful here.
Jessica Selvin
Similarly, to book a device on Pcloudy as well, we first have the authentication and then the get device list method. So, again, we have the device list API. We are passing all the parameters here on the request body like the token, the duration, which platform device, and whether it's available, right? So, all these parameters are getting passed here. And then, along with which, in case I can define the variable, three tries, and evaluation criteria, and test this. I can test it as an individual or test the entire flow. So, I'm just going to go ahead and run this.
Jessica Selvin
You can see all my three APIs were successfully run. Now, I can go into this view report and view a detailed report on each run, right? So, you'll be able to see how many passed, how many failed, how many skipped or are still in the running stage. right? You can also see what were the responses and the response time like the total and the average.
Jessica Selvin
Right? And you can see the results distribution. And you can see each and every test step whether what was the method and what was the final status response.
Jessica Selvin
And for every individual API as well, you can view a detailed request and response. You can see the response body here, the validation, and how many milliseconds or what was the response time and the size of the response body. So, each for each and every API, you can get all of this. So you can see that it does everything that Postman does along with giving you a detailed shareable report. So this is on API testing. Now how you can achieve all of this, right? So you can create your collections, right? You can simply go ahead and add your collection suite. All right, give all the APIs that I want to test in my collection.
Jessica Selvin
Right? So I've added my API collections here. Test suites here and I can simply go ahead and run my collection. And I can view the details here.
Jessica Selvin
And you can see the results of how many passed and how many failed. Right? So this is on how you will be able to scale it into collections. Now coming to how you would be able to do it cross-platform across your web and mobile as well, right? So let me quickly show you how you can create your collections on APIs and you can create your collections on for your mobile, web, and iOS test cases as well.
Jessica Selvin
So here we have a collection. So you can simply just select which project you want it from. So for example here I'm selecting maybe one web test case. I'm selecting a few Android test cases.
Jessica Selvin
And maybe one more from different application, right? So I have a cross-platform test case here. Right? And I can give the collection type and name. And we also have an option of enabling self-healing. So this will help in the maintenance of your test cases, where you can simply, in case if there have been any minor changes in your locators. All right, the self-healing agent will be able to identify that you're still referring to the same object and element and still pass your test cases instead of giving you false negatives.
Jessica Selvin
You can also select the device range that you want to run on. So, maybe you want to run on one Samsung device and another Google device. You can simply pick that and then select whether which browsers you want to run on, maybe one Mac and one Windows, and then you can just go ahead and create your collection. Right? So, we have the collection that we created here. You can also go ahead and now simply run it. Select which configuration of apps that you wanted. So, I've selected the banking app. All right, and then I can just trigger my run now.
Jessica Selvin
Right? So, you can see that the collection is triggered both for you can create your collections for APIs, you can create your collections for web and mobile, and monitor it all on our Pcloudy platform and get all your consolidated reports in the report section here. So, coming to viewing the reports, you can see all our reports here in this section. You can click on this icon to view the status of all your runs. Right? So, for example, I have a collection here.
Jessica Selvin
You can see the status of all your test cases. You can see that, I have so many past test cases, so many are in progress. I can click on any individual run and be able to see the videos and the text logs, Appium logs, and the AI analysis for each and every run. So, let me let me pick a report where the run is already completed. Yeah.
Jessica Selvin
So, you can see here this is for the IKEA app. We've done a run. You can see the text logs here along with the screenshots of each and every step. You can also see all your other logs like your Appium logs and the device logs. You will also be able to see any the AI the AI analysis as well, where you will see it will go through the different API logs the Appium logs and find if there are any issues. All right, so in case if here you can see just summarizing there's one unique error with the element not found. right, and then it you can also see what are those log locations. So it will give you provide you the detailed logs as well and give you suggested and recommended actions in order to fix that.
Jessica Selvin
And in case it encountered any stability issues as well, right? So any slow API responses or slow page loads right or maybe any unused navigations, you'll be able to see all of that as well. So this is how your reports look like. So you can see both on the API and the web and mobile side, these are the kind of reports you would be able to get all on a single platform. All shareable, so these reports are all completely shareable. you can simply log a bug into your Jira, Slack, or Trello directly from here as well and share link in order to share it across stakeholders. They don't need to be registered on Pcloudy platform.
Jessica Selvin
They will simply be able to view these reports. So I hope this gives you a good glimpse into how our solution helps in your mobile, web, as well as API testing replacing the tool fragmentation. Of course, there's a lot more and if you're curious for a more detailed demo, you can definitely reach out to us and we'll be happy to have more of a customized demo for you. Now coming back to and concluding, right? So what are some of the key takeaways that we've seen here, right?
Jessica Selvin
So firstly, we see how AI can really remove the code barrier. All right, so any QA team can build comprehensive tests and there's no scripting expertise needed. And you saw how in one platform I was able to do my I can run my manual test cases on the real devices. I can scale into automation with AI. I can also run my Android, iOS, and browser test cases on real infrastructure. And finally, I can get around my API test cases as well and have a centralized place for all my reporting. Right? And then we see with a single trigger how you can run your collections across your web, mobile, and APIs.
Jessica Selvin
And how this would drastically reduce your QA cycle time and scale your quality engineer. So, I hope this gives you some good insights. Thank you so much for being a great audience and I'm here to take up any questions. Thank you.
Jessica Selvin
Thank you.
Dinakar
Great, thank you, Jessica. We're just opening up the floor for some questions. If anybody has any questions, please do use the Q&A option at the bottom of the screen. And we'll be able to see your questions once you've posted them here.
Dinakar
Jessica, you can I think you can stop sharing your screen once we're Awesome. >> Sure, thank you. >> Okay, I can see one question here Jessica around integrations. So, we've invested heavily in Selenium or Playwright. how painful is the migration?
Dinakar
And also how And another question is about, how can this platform integrate with other tools such as, Jira, Jenkins, or GitHub Actions?
Jessica Selvin
Yeah, so if you've already invested heavily in automation, right? We're not trying to replace everything with AI, right? So, that would not be an optimized solution. so, the solution here would be to have more of a hybrid approach. Maybe for your current project, you can continue running your Selenium/Playwright on our real devices and browsers, right? So, we also have traditional automation support. it's not that you need to scale everything onto our platform and you're not locked in like that. right? So, you will be able to run your existing scripts from your framework and simply run it on our real devices and browsers.
Jessica Selvin
Now, coming to other integrations like your Jira and other bug tracking or management tools, we have seen those integrations with that like your Jira, Trello, Slack, and even GitHub Actions. right? And even from the CI/CD standpoint, we have seen those integrations with your Jenkins or Azure DevOps or Git workflow, right? We have seen this integration with all of them. Several of our enterprise customers are using this as their day in day out, right? To run their regressions on our infra. And I also see one more question from Radhakrishna, right? Where you were asking about which sites the device farms are currently hosted at? So, Pcloudy has its own device farm, right, so it is there in our own data centers. We are not, relying on any third-party solution to, for our infrastructure.
Jessica Selvin
All right, so you will simply be able to, go ahead and connect to our devices and all the devices that you I just showcased in the demo are hosted in our own data centers. We have several data center locations. We have a couple of them in India, Singapore, the Middle East, and the US, and upcoming in Canada and other locations as well. So, these are all our own, devices hosted in our data centers.
Dinakar
Here's an interesting question that I've come across again. can we run a subset of tests based on what code changed, or rather the full suite every single time?
Jessica Selvin
Yeah, so See, this is, you don't have to do that, right? We also have a QA orchestration tool, right, where you will simply be able to run your regressions much faster. You can also pair and choose, we can choose what, test cases you want to run from your collection, right? Maybe you just want to do a quick sanity check, and you have a few, runs, right, or a smoke test. All right, you will simply be able to, create, subsections from, your existing test we, our test cases from your project, and simply run those, test cases. So, it's not that you need to There's no limit on the number of collections you can create. You can create as many as you'd like and run based on the combinations you frequently.
Dinakar
Awesome. Great. Also, there's one question around real devices and emulators or simulators. So, do you support real device testing or only emulators or simulators?
Jessica Selvin
Yeah. So, as I mentioned, all these devices are real physical devices. Right? We generally do not support simulators and emulators because we believe that, real testing can be achieved primarily on real devices and all the scenarios that, you want to replicate and, identify bugs that will come from testing on real devices because that's where, you will be able to there is a stat that says around 40% of bugs cannot be detected on simulators and emulators. Right? So, you will be able to identify all that on real devices. So all the devices in our shareable platform real physical devices.
Dinakar
I think we're good on time and as well as the questions. So, I mean, if there are more questions that any of you folks want to ask, please do reach out to us, on our on our website. You can just click on the chat option and send us a send us your questions or you can even write to us. Once we Once the webinar closes, we'll kind of write back to you regarding the webinar and share the recording. So, you can write back to us on that same email ID, info@Pcloudy.com. So, yeah. Well, I'd like to take this moment to, thank Jessica for sharing these valuable insights. And also, I want to thank each one of our participants for joining us for this webinar.
Dinakar
We're sure that some of the insights that, Jessica has shared has sparked a few ideas. my recommendation would be to mull over these ideas, keep the conversation open, talk to us and discuss, and let's keep the conversation going. And like I mentioned earlier, do feel free to, reach out to us with any questions on using our chat option on the website, or write to us at info@Pcloudy.com, and we'll respond to your queries as soon as possible.
Dinakar
With that, thank you once again for being an engaging audience, and happy testing.
