Building Quality Software: AI-based testing approach with Jira and QMetry

The full transcript

Kavya (Director of Product Marketing, LambdaTest) - Hi, everyone. Welcome to another exciting session of the LambdaTest Experience (XP) Series. Through the XP Series, we dive into a world of insights and innovation featuring renowned industry experts and business leaders in the testing and QA ecosystem.

I'm your host, Kavya, Director of Product Marketing at LambdaTest, and it's a pleasure to have you with us today. Today we'll explore the transformative potential of AI in software testing. Before we get started, let me introduce you to our guest on the show, Mihir Lakhtaria, Business Analyst at QMetry.

Mihir brings a wealth of experience from his consulting background in large organizations, where he has honed his skills in identifying customer problems, designing solutions, and, of course, implementing enterprise-level tools.

At QMetry, Mihir is dedicated to helping clients solve their QA challenges using QMetry's advanced test management and test automation tools. His expertise lies in ensuring seamless integration and optimal performance, streamlining QA practices with AI-enabled digital quality for platforms.

In today's session, we'll delve into the key AI features that revolutionize test creation, execution, CI/CD, and code repository integrations. Mihir will also discuss predictive and prescriptive reporting, which covers test coverage and traceability. So, now let me hand over the mic to Mihir to share a bit about himself and walk us through today's Insightful session. Mihir, over to you.

Mihir Lakhtaria (Business Analyst, QMetry) - Thank you, Kavya, for that introduction. I'll introduce myself again. So, hi everyone; my name is Mihir. I'm working here as a Business Analyst at QMetry and I have a few years of experience in terms of technical consulting and Pre-Sales and also adopting various business tools and products and limitations.

So, during this journey of a few years, I got a chance to work with different customers with different backgrounds and different goals, and we always try to fulfill all those requirements and problems by implementing effective solutions.

Talking about myself, I am more passionate about leveraging technology to implement processes and deliver the value that the clients are intended to get from our site with the suit of available products. I look forward to sharing insights and experiences with all of you, especially in terms of AI-enabled strategies through QMetry products. Alright, I hope my screen is visible.

Kavya (Director of Product Marketing, LambdaTest) - Yes, it is.

Mihir Lakhtaria (Business Analyst, QMetry) - Welcome to this session. So in the set of artificial intelligence, artificial intelligence is transforming how we do software testing and manager test projects, making it faster and better, but which part of the test management with artificial intelligence will help us the most?

And how can we use artificial intelligence to supercharge the key test management features to speed up testing and improve the software quality? So today, in this session, we'll explore how QMetry test management and a suite of product-optimized testing and all overall management processes help teams scale their quality efficiently.

So, just a bit about myself: I’m Mihir Lakhtaria, and I have a few years of experience in terms of technology consulting, pre-sales, and product implementation as well as adoption.

In today's session, we'll try to dive deep into a point where we want to discuss more about the adoption of AI-enabled strategies through QMetry products and how we can leverage artificial intelligence, automation, and Jira in order to achieve all our test management goals using artificial intelligence.

So this is going to be the agenda for the day. Now, we'll try to cover as many topics as possible in terms of a detailed understanding of each and every topic that is mentioned on the screen.

We'll start from the basic understanding of how the current test management and what's the near future of test management look like. We'll also try to see what are the capabilities that a next-generation test management tool should have.

And we'll also try to cover AI use cases in test management tools and try to have a quick look at where QMetry stands in terms of delivering artificial intelligence features. We'll also see how these features are going to benefit the overall testing ecosystem and your testing team members and why QMetry is currently pioneering the artificial intelligence initiative. And finally, we'll open up for questions and answers if we have.

Right, so let's start with the basics in terms of how does the test management landscape look like. So these are the six pointers that I want to highlight and each analytic point represent a specific information that we should keep aware of. Now the first point is about the global software quality market group.

So according to a report from Gartner, the global software quality assurance market is on the trajectory of significant growth. From its 35.8 billion dollar US valuation in 2022, it's expected to reach a substantial 50.7 billion dollars by 2025. So that's a staggering 13.5% of compounded annual growth rate or CAGR.

Now this growth is not just about the numbers. It reflects the increasing importance of software quality in today's digital landscape, where reliable and secure software solutions are paramount for businesses.

Financially, this group signifies tremendous opportunities for organizations to capture a larger market share, capitalize on emerging technologies, and solidify their position as leaders in the software QA industry. And the second point is regarding the software quality for critical businesses.

And let's talk about the importance of software quality. So staggering 87% of IT executives believe that software quality is not just about important but it's also critical for business success. This underscores the fact that in a competitive market where customer experience and satisfaction are key differentiators, delivering high-quality software is non-negotiable.

It's not just about functionality, it's about building trust, maintaining brand reputation, and ensuring long-term business sustainability. Now, the third key point that is about the return on investment. So when it comes to return on investment in software quality, test management tools stand out with an average return on investment or ROI of 300%.

These tools not only streamline testing processes but also contribute significantly to cost savings, efficiency gain, and, ultimately, better software quality. Case studies and real-world examples showcase how organizations can achieve tangible benefits by leveraging the strategic value of a test management tool.

The fourth pointer is about the demand for continuous testing and DevOps. So the adoption of Agile development and DevOps practices has propelled the demand for continuous testing. So according to surveys, such as the one by Forester, revealed that a significant majority of organizations, that is 79% using Agile and 62% using DevOps, are embracing continuous quality practices.

Now continuous testing aligns with the agile principles of iterative development and rapid releases, ensuring that software meets evolving user expectations and market demands. So let's also talk about the benefits of having a test management tool, right?

Currently, organizations that leverage test management tools experience notable benefits, an approximately 50% reduction in the testing time and a remarkable 70% decrease in test defects. So these tools streamline test planning, execution, and reporting that leads to improved project timelines, reduced cost, and enhanced customer satisfaction.

The impact of efficient and effective testing processes cannot be overstated in delivering high-quality software solutions. The final analytic point that is regarding automation. So the World Quality Report forecasts that automation testing will constitute 50% of all testing activity in the foreseeable future.

The strengths reflect the growing recognition of test automation's benefits such as faster feedback loads, scalability, and repeatability, and successful implementation of test automation demonstrates its value in augmenting other QA practices and driving continuous improvement in software quality.

So, by understanding and embracing these strengths, organizations can navigate the complexities of the test management landscape, optimize their testing processes, and deliver software solutions that meet the highest quality standards while staying agile and competitive in today's fast-paced digital environment.

Now, once we have seen the current trends, let's also try to understand what are the challenges that are being faced by the software testing industry. So these are some of the pointers.

Now, the biggest challenge today is time to market. And I'm sure everyone would agree on this point that businesses today expect high-quality software to be delivered quickly. They have implemented Agile to respond to all these rapidly changing requirements and DevOps to meet the demand for speeds.

However, failing to apply continuous testing effectively leads to a long time to market or different large time cycles. I'd also like to bring out some numbers over here. So according to a PwC survey, 79% of corporate executives believe that accelerating time to market is crucial for staying competitive.

For institutions has to say is on an average, it takes traditional institutions 6-12 months to develop and launch a new product or a feature. Further reports from PWC states that startups that prioritize faster time to market are 2.7 times more likely to be successful.

So these are some of the astonishing numbers that we have seen over time. Now we have seen the trends, we are also witnessing the problems and how it can be solved by the next-generation test management or which are the capabilities that should be there in a next-generation test management tool.

Let's quickly have a look at all those pointers. So, the next-generation test management platform provides several key benefits, or it should have some of the benefits to call it as a next-generation test management tool. The first pointer is that today we don't use test management tools just to manage our executions or to manage our scenarios.

We use it to also track the overall testing at one place. So, there's a need of quality analytics that provides real-time smart analytics and advanced reporting capabilities. Now, this functionality caters to the needs of both technical and business users. The next-generation test management tool should also encompass AI capabilities to reduce the maintenance and creation process of all the assets.

For example, a bot that helps you identify duplicate scenarios, duplicate assets in your test management tool is very important, such as what QMetry offers. Now, QMetry offers QQbot. It's a platform that offers AI capabilities, such as auto prediction of duplicate test cases, and it also suggests predictive coverage with your requirements and test scenarios.

These capabilities greatly enhance reusability and streamline testing processes that optimize the efficiency and effectiveness of quality teams. The second parameter is having a scalable architecture.

As compared to the legacy tools, SaaS-based tools that offer high availability, configurable feature-based models for QA needs, and compliance certified are some of the key parameters that the current user base is looking out for whenever they are searching for their next test management tools.

They also want to integrate that current product with third-party tools using APIs and webhooks. And it also should adopt their choice of development methodologies as well.

So it's not that our teams are only going with Agile, and teams are only going with waterfall method. There are different methods that customers or users are using. And they want the test management tool to support all those kind of methodologies. Finally when you have our tool in place, we would also want to migrate our existing assets to the next tool that we are going to use.

So that's where the migration and adoption comes into picture. The next-generation tool should also offer capabilities where the legacy data or legacy information that can be easily migrated to the next-generation tool or tools such as cumulative test management that offers inbuilt migration capabilities that make sure that all of your data is available in your next tool without losing a single bit of your data.

The next-generation tool should also be modern in terms of user interface. It should be easier to onboard and it should be quick to adopt so that the user will have a flat learning curve as compared to the steep learning curve they had in the legacy tools. So these are some of the parameters and capabilities that should be there when we talk about the next-generation test management.

Let me also highlight where QMetry comes into the picture when we talk about next-generation test management tools. So, QMetry test management comes with QMetry intelligence, or it is powered by generative AI features. So, it's all about boosting your quality quickly and efficiently.

So, we have made it powerful, scalable, and compliance-focused, all to help you achieve quality at speed and with a better return on investment. From the GenAI perspective, we have implemented advanced functionalities such as smart search, auto test case generation, and flaky test case detection.

Now, these capabilities are designed to streamline testing processes, enhance test case creation efficiency, and improve the overall test result reliability. We have also integrated it deeply with popular requirement and defect trackers, automation tools, and CI/CD pipelines platforms so that you can keep testing seamlessly. This means you can maintain high quality while moving at the speed demanded by the modern development process.

What's more here is we have built in features like multi-level approval workflow with e-signature for CFR part-11 compliance. And we also offer customization options to fit your specific testing needs while staying competitive with industry regulations. It's about making testing not just effective but also compliant and flexible for your businesses. That's what we follow.

So we have covered what are the capabilities that should be there in a next-generation test management tool. Now what are the artificial intelligence-based use cases that the current customer base is looking out for whenever they are trying to switch to a different tool or whenever they are evaluating a different tool or that upgrade?

So here are some of the AI use cases that are currently in demand and the customer or the prospect that are looking to change to a next-generation test management tool. These are some of the features that they are looking for.

So let's start with the first, I'll try to give you a glimpse of what are the features that they're looking for and what are the features that can help the quality teams in order to perform better and more efficiently. So the first point is about the test case being created automatically.

Now, if we talk about Jira, since we want to include Jira in today's point as well. So Jira is currently used as a project management tool by a lot of organizations. And there you have your requirements to be created as stories, tasks, epics, and so on.

Now, that's where a scenario comes into picture for your development team is they would need to understand the story thoroughly, and they will then need to create a schedule supporting. Now where QMetry comes into picture or what are the features that could aid these process is automatic test case generation based on your user story description.

So what it does is it analyzes the user story and creates your test cases automatically. Now this feature does not replace your existing test effort, but it always works as a great helping hand when you are creating your scenarios at first, right?

So that is something that is auto-created of test cases and steps using stored description. Next is how designing test suite with test case linkages. So in today's time, it's most essential part is to discover which are the scenarios that needs to be covered or which are the scenarios that need to be executed to fulfill a specific requirement.

That's where the smart design of test suite comes into picture. So design test suite with test case link based on the execution objective provided by a tester to reduce manual effort and ensure that comprehensive coverage is there.

The third pointer is about empowering testers to learn on the go with artificial intelligence powered search and conversational both because you whenever you are testing your scenarios, you always want a helping hand in terms of what needs to be done, how it needs to be done and that's where the smart search comes into picture.

It provides you with this flexibility to search on the go whenever you want to search through any documentation and SOPs whenever you are testing right now. Next is about auto creation of defects based on description and steps about failed execution.

So this reduces your manual effort that is required by testers and facilitates faster issue resolution. Evaluate test case flakiness and success rates to identify and address flaky test cases, enhancing overall testing confidence, and utilize an AI assistant to summarize reports and extract actionable insight, identifying patterns, trends, and areas that require your attention.

You can also use AI features to improve the accuracy of your existing test cases through AI-driven enhancement to ensure the reliability of your test cases are there. And finally you can utilize artificial intelligence to generate test data for test case executions that minimizes your manual effort.

And it also introduces a sense of diverse data generation that supports your comprehensive test coverage. So this essentially covers all the use cases that should be there in your next-generation test management tool. Now, QMetry has already delivered some of the features.

Let's quickly have a look at those features that are being available in QMetry. So I'll switch to a window right now. And let's have the first feature that we call as auto test case generation. So as I was saying that your business users or your development team members are working in your Jira, creating requirements and that needs to be aware.

Now Jira already has a lot of features available for you, for your business users, for your development team members to work as a helping hand. But when it comes to testing team members or a testing team, there are few features that they can do this. So that's why we have introduced a lot of useful and innovative AI features that can help testing teams work more effectively and efficiently.

So the first feature that I am going to show you today is called as auto-generation of test cases. Now this works simply as the name suggests, it will create your test cases based on your user stories. Now let's assume that your development team or your business users have created some user story.

Now here I have taken an example of a simple food order delivery system. So you can order your food online and that will be delivered to you. And your business analyst or your product manager has created some use cases and acceptance criteria as well.

Now, if I were to go with the traditional approach, the story would be then analyzed by various QA team members and they will create test scenarios around it. But if you want a helping hand, that's where QMetry intelligence comes into picture.

So we have an option that directly analyzes your user story and creates test cases around it. So if you click on this generate test cases with QMetry, it will then analyze all the scenarios that are present.

Now let's have a look at primitive test management AI capabilities that the feature has to offer regarding artificial intelligence and how it makes it more effective and efficient for your quality team members.

Now Jira always has these features around making it more easier to search, making it more easier to create a summary out of all the content that is present on your screen. And Jira has always delivered those features.

But if I talk specifically about testing team members, there are a few features available and that's where the QMetry AI feature comes into the picture. So let me start with auto-generation of test case. Now this feature specifically works on your customers user stories and it will help you getting started about creation of test cases.

So I have taken a simple example of an online food order delivery system that will deliver food based on your request from your desired restaurant or cafes. Now in this case, my business analyst or my product owner has created a user story. They have also created a use case and acceptance criteria.

And right here, once you install the QMetry application, you'll have this button to generate test cases with QMetry Intelligence. Now, as soon as you click on the button, you'll see that the AI model will then analyze all the user story details in terms of use cases, acceptance criteria, and so on.

And based on its analysis, it will then decide how many number of test cases it should generate. So based on its analysis, there should be nine test cases generated for My User Story. So you can guess that it will be nine different test cases that your manual testing team has to create. So it's a great helping hand if you want to get started about creation of test cases based on your user story.

So we can see right now it has created nine different test cases. We can see all test cases available to us along with description and precondition and we can modify details as well.

So it's not always necessary that you get what you see. You can always modify all these details based on your needs. And it's not just basic test cases. It covers all various edge cases, all scenarios, and it has detailed test steps created for all your scenarios.

Now you can always go ahead and accept or decline those test cases and those won't be part of your creation process. So I can decline for example, I will decline two test cases. And now if I want to create the furthest test, you can click on create test cases and it will create test cases that we have selected.

At the same time, it will link automatically with the user story that I wanted to work with. So that's one of the great feature for your testing team. They don't have to waste time in terms of considering different scenarios based on the story description, it can generate test cases and it can also link with your user story as well.

So this is about auto-generation of test cases. Now, if I talk more about test management tool capabilities, then there are scenarios where organization involved in repetitive execution of the same test case. Now, that's where they tend to encounter flakiness or less successful test cases.

So to mitigate that problem, QMetry has a feature that is called as flaky score and success rate calculation. So if I just show you the configuration of it, a flaky test is something that is being executed repetitively, but still the execution result is non-deterministic that means you cannot predict whether the test case is going to be pass or fail.

And it's essential that to identify these flaky test cases well within your pipeline earlier. You don't want that test to be bottlenecked in your testing practices. That's where the identification of this flaky score comes in the picture.

So you can configure on the screen that how many test cases it should consider as flaky. You can also set which environment, which build or which release you want to consider for this flaky test case generation. And you can even configure which test case status to be considered as pass and which test case status to be considered as fail.

And once you do that, you can see each test case has been assigned with a flaky score number. So if I show you an example, I'll quickly jump to my screen and we can see a success rate available right over here, right? So let me also explain what is success rate. Now success rate is a measure or a simple calculation of.

Out of the overall execution, what is the percentage of your executions getting passed? So it's a key in addition to what you can see out of this Plaky score setting. So based on the numbers that you can see on your screen, if it is 80% then out of 100 execution, 80 executions are passed, right? It is just a calculation of how your test cases are behaving whenever they are being executed. For example, this 49% indicates that this test case is more frequently failing.

So you can go ahead and repair the test case if needed. And again, you can always configure all these settings, all right on this configuration screen. So now if you see each test case will have its own flaky score and success rate associated with it. If I show you a simple example, let me go ahead and open up the test case. Let's say I'll open a test case with, let's say this one. So this is my test case.

Right on the screen, I can have my flaky score and success rate display if I wanted to. Let me go back and see if that is available. So I'll just quickly have a list available as well. So I'll sort it out based on my flakiness.

And here I can see a flaky number or flakiness score has been assigned to my test cases. So each test case has its own number associated with it. And higher the number, we present the higher probability of the test case getting flaky.

If I click on that number, it will also give me all the recent executions that were incorporated to build this number. So this point 23 represent this is low flakiness yet this can be considered as flaky compared to an absolute non-flaky test. And next to your flaky score, you have success rate measure available.

So for 80% meaning out of 100 executions, 80% of the test cases are passed. So by utilizing both of them effectively you are removing the bottleneck in your testing practices and identifying flaky test cases very in advance will help you mitigate all the problems that you can face in the later part of your testing activities.

So that's why this flakiness and success rate comes in the picture. As I said earlier, it's always essential for large teams or even small teams to abide by any SOPs or any documentation that they are testing. That's why the smart search feature comes in the picture. So you can put in your queries over here.

For example, if you want to search how to import test cases in symmetry, you can simply write your query in a human readable format and it will give you results accordingly. Now here you can also include your product and project.

For example, if you are working for a large bank and there are some SOPs around your testing practices, you can even put in your questions over here and our AI model will then search through those project and product documentation.

That is how the single search bar will work as a single source of truth for your testing activities as well as your SOPs or any other kind of project-related activities. So that is about this smart search feature. So these are some of the artificial intelligence enabled features that can help your testing teams work more effectively whenever they are involved in tests.

Now, let's also discuss about the benefits of having an artificial intelligence enabled test management tools. So right from this slide, we can make an inference that it is good to have artificial intelligence tool embedded within your practices because it has a lot of good features that can help you minimize your effort, minimize your workload, and so that you can focus on more productive tasks that are present at your place.

So using these tools, you can always collaborate seamlessly across your teams. You can even achieve reduced time to market using these AI enabled features. You can always optimize your return on investment whenever you are investing in these kind of tools that help you manage your test cases.

At the same time, it can also help as a helping hand in all kinds of your testing processes. And using these kind of tools, you can always scale with ease. So these are some of the tangible benefits that you can achieve by leveraging AI enabled test management. Now, finally, why QMetry?

So QMetry has three products available, right? And QMetry stands out as an ideal choice because it offers comprehensive coverage of all testing products. It seamlessly integrates within its suite and the ability to integrate with external tool is something that our customers love us for.

A platform places a heavy emphasis on AI -driven testing providing capabilities that are unmatched in the market. This means our customers benefit from cutting-edge AI technologies that enhance testing processes and deliver superior results, setting them apart from competitors. All right, so I think I'm done with my presentation. We can open up for questions and answers.

Kavya (Director of Product Marketing, LambdaTest) - Thank you so much, Mihir. That was really insightful. Jumping onto the questions now. Great, so the first question that we have is, how does AI address the specific challenges of large organizations compared to smaller teams?

Mihir Lakhtaria (Business Analyst, QMetry) - Right. So when we talk about larger organizations, they have their own set of requirements. They have their own set of challenges that they work with. Whenever we are involved in a large organization, they have their own requirement and they don't want to compromise on that. Right. So that is one thing.

But when we talk about AI specific challenges, the challenges that can be solved by AI specifically, then one of the challenges that we found was having duplicate assets because in larger organizations, there's a large set of number of users who are actively using the tool.

And there's always this possibility that they create duplicate test cases and that creates a clumsy structure within the system, right? Because you would have, let's say, five to six copies of the same scenarios that you want to test.

So in that case, the duplicate identification of QQbot. Now, QQbot is a feature offered by QMetry that helps you identify duplicate test cases. So that's something that the largest organizations have always benefited from.

Again, what we have seen in flaky score and success rate, right? If you talk about larger organization, they won't repeatedly execute the same set of test cases day in, day out basis. And that's where the possibility of generating flaky test scenarios comes into picture.

So utilizing these features, a lot of customers have benefited that they have removed the bottleneck within their testing practices, and they could move much faster when it comes to running repetitive test cases over and over.

There's also a feature about smart search what we have seen. So when you have large organization and you will have always some set of SOPs, some set of documentation, some SRS documents that you need to refer to. For example, some of the conditions are mentioned in your SRS document that you need to refer to whenever you are testing.

So these are some of the use cases where this product search and project search comes in the picture. Now this project search and product search also works as a helping hand whenever you have any query regarding human products or even generic questions. So these are some of the features that are being leveraged by our customers and they do love these features.

Kavya (Director of Product Marketing, LambdaTest) - Thank you so much, Mihir. So essentially, AI can be tailored to fit different organizations that are at different levels in terms of scale and complexity, as well as I can understand. And to sum up what you said, you know, using QQbot, and other products, for instance, your enterprise customers are able to identify duplicate test cases, flaky test scores and of course, utilize smart searches.

That's very insightful. Thank you. Moving on to the next question, how can human testers use AI insights to enhance their strategies and focus on where their expertise is most valuable?

Mihir Lakhtaria (Business Analyst, QMetry) - So when we talk about AI, it's a vast topic that you can discuss. AI has a lot of useful features that can be utilized to minimize the mundane or repetitive tasks each QA member does on a daily basis.

For example, we can use, as I said, the QQbot features to remove duplicate test cases so that they can reutilize it. So you are not wasting your time in terms of creating duplicate assets. You can even use the AI features such as automatic test cases to help you getting started.

It's not a total replacement of your testing team members efforts. That's not what it means. We want to minimize the effort just to get started. You want to cover all the scenarios that are present, what your business owners want you to cover. And that's where this AI generated test cases would come into picture. Now, we have also seen that there are a lot of tools available in the market that offers a lot of reporting.

In my opinion, I think that reporting is always fine, but if we could always draw out some kind of inference that what should be done next, right? That is something that AI can help us with. And that's also something that we are looking forward to when we are talking about giving more useful features in terms of AI.

So that is that. And again, if you talk about testing on multiple parameters, let's say you are testing a feature it's always a good thing to run the same set of test case over multiple parameters. So that's why test data comes in the picture.

And artificial intelligence can create data in such a way that we are trying to, or we can cover almost all kinds of edge case scenarios using those test data. So I think these are some of the points where AI can help us greatly. And as always, it's always under research and development to identify more use cases of AI.

Kavya (Director of Product Marketing, LambdaTest) - Thank you, that is very fascinating. What you are basically trying to do is leveraging AI so as to augment the existing human expertise, so as to sort of take testing to another level altogether. And of course, moving on to the next question, how does QMetry's AI testing enhance Jira workflows for better test management?

Mihir Lakhtaria (Business Analyst, QMetry) - So now Jira always has its own AI features and they are always the first one to bring out unique features, useful features out there. For example, Jira has useful features like generative AI to create content for your pages or even for the different modules available. So Jira offers these kinds of capabilities. It also offers some natural language filters that can replace to some extent the JQL as well.

And it can also summarize all your content in a few lines. So these are some of the features. And this summarizes something what we have also trying to deliver. So they also have features like mutual language automation.

So you can create any level of automation based on just normal human language. For example, if a new issue is created, you want to do something like this. This should be assigned. And all those kinds of automation can be done by just providing human-readable comments.

Now, QMetry’s AI feature can help these teams, testing teams shift left earlier in the testing cycle. Meet features for automatically creating test scenarios for a given user stories or identifying flaky tests that could become a bottleneck for your next release.

So essentially, we want to just empower all the testing team members by providing useful AI features and that can help the reduced time to market and reduce time to deliver the product with high quality. Now, when we talk about how QMetry can integrate with Jira. So QMetry has a lot of AI features inbuilt that can work as a helping hand to your Jira ecosystem users as well.

For example, if I talk about auto-generation of test cases, so that it will not only give you a set of test cases, but even your business owners can also take inferences out on those test cases because it's always good to have a second person or second review on your created content or maybe your user stories or any tasks you create.

I think that's where this integration comes into the picture, and QMetry also integrates really tightly in terms of Jira so that your testing activities are also available right within your Jira ecosystem. So that's where, by leveraging both the tools and the tool's powerful capabilities, they can work with harmony and generate great results.

Kavya (Director of Product Marketing, LambdaTest) - That is very interesting. Thank you for highlighting not just QMetry's AI features but also what Jira has implemented so far. I'm pretty sure that it would make the lives of testers and devs much more efficient and smoother.

Moving on to the next question, would you like to share some examples of how QMetry's AI testing reduces software cycles in Fortune 500 companies? Of course, you have thrown a bit of light on the very first question that I asked, but it would be great to see some more specific examples.

Mihir Lakhtaria (Business Analyst, QMetry) - So when we talk about last organization of Fortune 500 companies, as I said, they would have their own set of requirements. They would have their own set of methods that they wanted to see being implemented in the test management tool that they are going to use.

Now, QMetry already has some of the features embedded right within the system. For example, features like e-signature, test case dependency, risk-based analysis, and granular permission levels so that, for example, you don't want someone to perform any action.

So these are some of the features that are required by these large organizations. So this is also amplified by the various compliance and certification laws, for example, the software certification, ISO compliance. So a qubit tree also complies with all these compliance norms, but these are the general use case or requirements that are given to us whenever we of any Fortune 500 or any large companies.

So in conjunction with these compliance, what AI features, community test management solution offers is, it becomes one of the best tool to look out after. So I'll quickly try to list out some of the companies, not naming them specifically, so I'll just relate with the industry.

So one of the large If I remember correctly, one of the large e-commerce giant was struggling with duplicate test cases as you know, hundreds of testing team members were working simultaneously. They were facing a challenge in terms of streamlining the process of creating test cases at the same time, how to reutilize their existing test case.

And that's where they encountered various duplicate scenarios, different, you know, they have to then create a set of team members just to handle those. So that's why this duplicate test case comes into the picture. The QQbot helped them remove almost all the duplicate test cases. The second was about a telecom giant.

So there was a telecom giantt. They were, as you remember, if you know that telecom industry is something that is minded by various regulations and they always have to test out on various parameters in the testing line.

So that's where they encountered the issue of flaky test cases as tests were being run repetitively every day and numerous times a day. Now using the flaky score analysis mechanism, they were able to identify flaky test case before they become a hurdle and that could slow down the overall testing speed. So that's something that flaky score helped them achieve, you know, deduced time to debris.

The third one would be, I'll try to point out one of the large banks that we have worked with. So they had a strict execution guideline that all testing team members should apply for. Using the smart search feature, they were able to use the time taken for the testers to jumble between multiple documents and quickly refer to the guidelines provided in just a single click.

So these are some of the AI features that I'm talking about specifically that help our Fortune 500 customers work more effectively and efficiently using the AI.

Kavya (Director of Product Marketing, LambdaTest) - Thanks, Mihir. That's again very insightful. I'm sure our audience would also benefit from learning about how you have been supporting large scale enterprises to utilize the power of AI. Now, moving ahead to the last question of the day, what future AI advancements in software testing is QMetry working on currently?

Mihir Lakhtaria (Business Analyst, QMetry) - So as you see, we have already delivered a few features and it's not always about delivering new features, but also to improve whatever is out there, right? Because AI is something that's not stagnant. It will flow just like water. You have to keep updated with the latest trends and technologies that are available in the market.

So, QMetry is always working on two things. That is one, introducing innovative and useful AI features at the same time. We also try to always update and upgrade to whatever latest available version in terms of being a training with a middle-aged data or utilizing different models to improve results of capabilities.

So we are planning to bring out new features to improve overall testing processes. So I cannot list out all the features here, but just to give you all a glimpse. So QMetry is also planning to help reporting side of things. So we have seen that where how users can improve their existing test cases with AI models.

For example, if you have hundreds of test cases, you would want a way through which you can at least rank some of or make it perfect, right? Because you always wanted to make sure that the test case that you're creating is up to the mark. It has created, it covers all the scenarios and everything. So that's where this AI enabled suggestion would come into picture. And that's what we are currently working on.

So, QMetry is also working on a feature that will suggest based on your requirement, a test suite. Now, whenever we are talking about any feature that is going to come, we want to test it out as extensively as possible. And that's where the challenge comes in, that we want to take into consideration all possible scenarios that could encounter during that execution.

That's where this QMetry auto-generation of test suite comes into picture where based on the basic requirement or human readable language, let's say you give some basic details like I want to generate a regression test suite for my upcoming release and based on your details or based on your criteria, the test management tool will automatically create a test suite for you, considering all possible scenarios and all test cases that you have.

So that again becomes a great helping hand for your testing team whenever we talk about creating more extensive or exhaustive list of test cases that can help you cover all aspects of your testing. So these are some of the features that we have.

Kavya (Director of Product Marketing, LambdaTest) - Thank you so much for sharing the insights, Mihir. It is great to see how organizations such as Scumentary Lambda Test are pushing the boundaries of AI in testing, for instance. And of course, I do look forward to seeing more of it in action in the future.

I'm sure our audience would also be very interested in seeing what you're up next. So, as we wrap up today's session, I would like to thank Mihir for joining us and sharing valuable insights on AI-based testing approaches.

To all our audience, thank you for joining and stay tuned for more episodes of LambdaTest Experience (XP) Series, where we continue to bring you the latest trends and expert insights in the testing and QA ecosystem. Mihir, if there are any parting words that you might want to share with our audience, over to you.

Mihir Lakhtaria (Business Analyst, QMetry) - Right, so thank you and thank you LambdaTest for giving me this opportunity to share the insights and to give me a chance to share what QMetry is up to and how we are moving ahead with the recent AI advancement and what are the things that we are looking forward to. So yes, thank you for that opportunity.

Kavya (Director of Product Marketing, LambdaTest) - Awesome. And audience, if you have any further questions or need additional information, do reach out to Mihir on LinkedIn. We will be sharing his LinkedIn profile in the social media handles as well as on other platforms. Thanks again Mihir for joining us today. It's been a pleasure hosting you. Bye.

Mihir Lakhtaria (Business Analyst, QMetry) - Thank you.