Hi everyone. My name is Mohammad Nja. I work as a solutions architect at Amazon Web Services. So thank you for joining for today's session. So today I will be talking about Amazon Queue Developer and how is it integrated with GitLab to create a next level developer experience. So before diving into the D details, I wanted to share a report from GitLab Global DevSecOps which was shared last year, which says that 74 percentage of organization using AI for software development reports the desire to consolidate their tool chain. So we have seen the AI tools have been part of our personal life as well as. Our work life for many months and couple of years now. So what we have seen from customers is a strong requirement or a need to consolidate, some of the tools so they can perform their tasks related to software development in a single tool rather than, using multiple tasks, which increases the complexity as well as causing confusion. That has been a need that customers have been, sharing with us. So how can we help the people who have been using KIT Lab, so that, that's the main part of our today's session. So that's where GitLab Duo with Amazon Q mbs, right? So we know that GitLab is a very popular DevSecOps platform that has been used by software developers and IT professionals all across the globe. Now with integration with Amazon Queue you have the ability to accelerate your overall software development lifecycle, whether it's transforming your legacy code or in order to, you have an idea and you wanted to create code and all the way to, raising a merge request in GitLab, that all can be accelerated, using Amazon Queue. And also you, if you want to conduct security code reviews. For your code, Amazon queue can also help with that. So the aim of using Amazon Queue is to accelerate your software development lifecycle. So we will talk about some of this in the coming slides. We also have like few demo videos that can help you in better understanding of the features of Amazon Queue. And one more important thing is that as of today, the Amazon queue is for GitLab customers is only available for GitLab self-managed ultimate customers. So I also wanted to talk about some of the core features of Amazon Queue when using it along with GitLab Duo. So the first one is streamlining software development, right? As I said before, if you have an idea, so you have a, code triple which you have in GitLab and say there is a new. Issue that you have created an idea, and with the use of Amazon Q can. Look at that issue, see the descriptions and create a code, for you, which you can then review yourself and, add or remove anything that you don't, you want to update and then send all the way to, MER request. So we will see again then in a demo video coming up. And the second one is optimizing your code, right? You can use Amazon queue to generate unit test from a merge request. Another important thing is like maximizing the code quality and security. So with Amazon Queue you can perform, high quality security code reviews. And lastly, you can modernize your code, you can transform your legacy Java applications that are using eight or 11 version, and then. Transfer me to Channel 17 in minutes. And earlier we know that it can take up to weeks or in some cases, depending upon the size of the code, it can take up to months. But we'll see, shortly how Amazon Q working along with Kit Lab can accelerate that transformation. So coming to the first feature is idea to merge request, right? So what are the benefits? And it is quite clear, right? It's faster feature implementation cycles. So if your business requirement needs a particular new feature that you want to deliver it, quickly with the help of Amazon, QU can deliver it much faster now and. Core quality, right? It gives you and another perspective from the A R AI code AI tools such as Amazon Queue to ensure that the quality of the code is high and it doesn't contain any, vulnerabilities, which it can detect through the unit security code code scanning, et cetera. And. And lastly, it improves the developer productivity, right? And thus the overall aim of using such tools, right? So for the demo, we have a sample. GitLab page where we have a sample project with types script, files. So now what we are going to do is we will be creating an issue, which is essentially an idea that we want to integrate into this project. So what we'll be doing is we'll be creating an issue on the top left and we can. Title. So in this case it's an adding a new signup flow to the website, as you can see here. And and you create an account for the user that takes an email and password and username add a flow for existing users to sign in. And this should be linked from the whole page, right? So those are the few descriptions that we have added into our the new issue, right? Now what we are going to do is like once we created it, the issue will be invoking Amazon Q using forward slash q and Dev. And once we enter that, we will see anime a message from Amazon Queue Service saying, I am working on generating code for this issue. I'll update this comment at Open Emerge, request when I am done. So in the background, it's going to look at the title, it's going to look at the description that you have entered, and then it's generating. Quote on behalf of you, and after a few seconds you will see an open merch request, right? So let's take a look. So you can see that it has finished generating the proposed code changes and opened a merge request. So we can go to that merge request now, and you can see it, it documents or it updates all those comments in the relevant, issues or merge requests. So it's easier for us to understand what's happening in the background on what exactly Amazon Queue service is doing. So you can see that the code has been implemented. So the other important step is to review the code changes, right? So you go through the code and see what are the additional code that it has been degraded, and see if there is anything that's missing. And basically doing a quick check of the quality of the code, right? So in this case, what we are what we're seeing is like we, we are trying to include one more small feature into the code. So in this code there is no logging. So this, in this term you can see we are adding a comment again, saying at logging and when we. After we add the comment and then we again invoke the Amazon Q Service slash Q Dev. What it does is the same thing. It reviews the code, it reviews your comment, and then it creates a code, based on your requirement. Right? So again, going back to the changes, you can see there is now a new file called Log at or ts, which is. Very recently created for you and you can review that code and if you're satisfied with it, you can basically upload the moist request. So this is one of the features of FAM using Amazon Q with kit Lab. So the next one is J application Modernization. Let's say in your current in organization, you have your applications that are using Java eight or Java 11 applications. So if you're looking to modernize or transform the versions to Java 17 quickly, then Amazon Q is a tool that you want to use. The specialty of using this tool is, it's not just transform your code, but it does that in a well-documented way, right? It creates an upgrade plan and it generates, the ready to review MER request while maintaining full traceability. So you have that full clear documentation of what happened. What are the dependencies that he it removed? What are the dependencies that are, it has upgraded. All sort of information that you would need will be documented along with the actual transformation of the code. So now let's take a look at that demo. All right, so now again, we have a new issue which we have given a title called Upgrade Project to Java 17. And then what we are going to do is give a in the dis in the description, we will be invoking. Amazon q in this case will be invoking transform. Earlier we used dev, DEV in order to generate code, but in this one we are transforming the code, right? So that's why we are using transform. And then when we create the issue you will see. What are the different steps that Amazon Queue will perform? So the first one is I'm running the GitLab CSAD job required. Transform your code and then it'll upload your code, generate a transformation plan. It'll transform your code and then generate a merge request, right? So we'll see. What are the, what's the accreditation plan and the documentation that Amazon Q provides? So you can see that it has started giving output and very quickly you can see that it, it analyzes the source code. It says the lines of code in your application, dependencies that needs to be replaced, a file should be changed, and the plan transformation changes all those steps by different steps that it'll take in order to upgrade. And once it's created the cre transformation plan, the next step is actually transforming the code. And you will see that, yeah. It has finally created that. Merge request for the upgraded upgrading to the Java 17. And as you can see in the screen it details all those additional details that you'll recur like the additional code that has been replaced. What are the files that has been changed and what are the next steps that it'll do? Asks you to do right? You should always review the code and see the change that it has made to the application. And again, but most of the times you might have to make your small changes in some of the files. But the aim of using Amazon Q is to accelerate your overall software lifecycle development, right? And that's what we are seeing now. You can see that it has paused the, different stages of your pipeline and it yeah. So this is one of the other demo that I wanted to show you how you can utilize Amazon Q to upgrade your Java legacy applications to two, which have a 17. So moving on to the next and final feature is the code reviews. So we know that when we develop the code, we do the, peer reviewing, and other measures. But with the, gen ai we can. Do much better, right? That's basically using Amazon queue services such like that to review your existing code and it'll suggest are there any vulnerabilities? Are there any scope fori optimizing your code, to make it more resilient, more secure in nature. So for the demo, what we are going to see is we have emerge requests, right? And we, with all the code that we have written down or generated by Amazon queue. And then what we are going to do is like review that code to check for quality or, security issues. So for that purpose, we'll be using forward slash q review. And when you comment that behind the screen. It scans the code and it gives you a detailed information on the the vulnerabilities it has detected. And it also highlight the piece of code in your actual code base to see. This is where you have that vulnerability. So in this use case, you can see that it has, it is seeing that is a possibility of SQL injection attacks because of this code. And what you can do is like you can review the findings and then. You can fix it then and there, right? And for that we use the fixed command. And when you add that command, it'll generate the updated code and you can review the code changes again and ensure that it's all good. And then, apply the suggestion. So this is one of the ways you can. Utilize Amazon queue to, basically review your code for, quality checks and other security vulnerabilities. So that's all what I wanted to talk to you about, just highlighting the main core features of Amazon Queue and if you're using GitLab in your organization. And if you're using AWS, what I would suggest is, give it, try with Amazon Queue and see how, if it helps in your, software development for your organization. So feel free to scan the Q QR code to learn more about this integration. And as I said before, as of today, it's only available for self-managed ultimate customers. But yeah, thank you. Thank you so much for taking part in this session. I hope it has been helpful and I hope you have a good rest of the day in the platform engineering conference. Thank you.