Search the Community

Today, we're excited to announce the availability of a new capability of Amazon Q to analyze issues for complexity and propose splitting the work into separate tasks. View the full article

Today Amazon CodeCatalyst announces support for approval gates within a workflow. Workflows provide an automated procedure for building, testing, and deploying code as part of a continuous integration and continuous delivery (CI/CD) system. Approval gates pause the workflow run at the gate so that a user can validate whether it should be allowed to proceed. View the full article

Amazon Q feature development enables teams using Amazon CodeCatalyst to scale with AI to assist developers in completing everyday software development tasks. Developers can now go from an idea in an issue to a fully tested, merge-ready, running application code in a Pull Request (PR) with natural language inputs in a few clicks. Developers can also provide feedback to Amazon Q directly on the published pull request and ask it to generate a new revision. If the code change falls short of expectations, a new development environment can be created directly from the pull request, necessary adjustments can be made manually, a new revision published, and proceed with the merge upon approval. In this blog, we will walk through a use case leveraging the Modern three-tier web application blueprint, and adding a feature to the web application. We’ll leverage Amazon Q feature development to quickly go from Idea to PR. We also suggest following the steps outlined below in this blog in your own application so you can gain a better understanding of how you can use this feature in your daily work. Solution Overview Amazon Q feature development is integrated into CodeCatalyst. Figure 1 details how users can assign Amazon Q an issue. When assigning the issue, users answer a few preliminary questions and Amazon Q outputs the proposed approach, where users can either approve or provide additional feedback to Amazon Q. Once approved, Amazon Q will generate a PR where users can review, revise, and merge the PR into the repository. Figure 1: Amazon Q feature development workflow Prerequisites Although we will walk through a sample use case in this blog using a Blueprint from CodeCatalyst, after, we encourage you to try this with your own application so you can gain hands-on experience with utilizing this feature. If you are using CodeCatalyst for the first time, you’ll need: A CodeCatalyst space Amazon Q feature development in CodeCatalyst is currently in preview. To access this feature, ensure that you are using the Standard or Enterprise tier and the generative AI feature is enabled in your Space Walkthrough Step 1: Creating the blueprint In this blog, we’ll leverage the Modern three-tier web application blueprint to walk through a sample use case. This blueprint creates a Mythical Mysfits three-tier web application with modular presentation, application, and data layers. Figure 2: Creating a new Modern three-tier application blueprint First, within your space click “Create Project” and select the Modern three-tier web application CodeCatalyst Blueprint as shown above in Figure 2. Enter a Project name and select: Lambda for the Compute Platform and Amplify Hosting for Frontend Hosting Options. Additionally, ensure your AWS account is selected along with creating a new IAM Role. Once the project is finished creating, the application will deploy via a CodeCatalyst workflow, assuming the AWS account and IAM role were setup correctly. The deployed application will be similar to the Mythical Mysfits website. Step 2: Create a new issue The Product Manager (PM) has asked us to add a feature to the newly created application, which entails creating the ability to add new mythical creatures. The PM has provided a detailed description to get started. In the Issues section of our new project, click Create Issue For the Issue title, enter “Ability to add a new mythical creature” and for the Description enter “Users should be able to add a new mythical creature to the website. There should be a new Add button on the UI, when prompted should allow the user to fill in Name, Age, Description, Good/Evil, Lawful/Chaotic, Species, Profile Image URI and thumbnail Image URI for the new creature. When the user clicks save, the application should leverage the existing API in app.py to save the new creature to the DynamoDB table.” Furthermore, click Assign to Amazon Q as shown below in Figure 3. Figure 3: Assigning a new issue to Amazon Q Lastly, enable the Require Amazon Q to stop after each step and await review of its work. In this use case, we do not anticipate having any changes to our workflow files to support this new feature so we will leave the Allow Amazon Q to modify workflow files disabled as shown below in Figure 4. Click Create Issue and Amazon Q will get started. Figure 4: Configurations for assigning Amazon Q Step 3: Review Amazon Qs Approach After a few minutes, Amazon Q will generate its understanding of the project in the Background section as well as an Approach to make the changes for the issue you created as show in Figure 5 below (**Note: The Background and Approach generated for you may be different than what is shown in Figure 5 below). We have the option to proceed as is or can reply to the Approach via a Comment to provide feedback so Amazon Q can refine it to align better with the use case. Figure 5: Reviewing Amazon Qs Background and Approach In the approach, we notice Amazon Q is suggesting it will create a new method to create and save the new item to the table, but we already have an existing method. We decide to leave feedback as show in Figure 6 letting Amazon Q know the existing method should be leveraged. Figure 6: Provide feedback to Approach Amazon Q will now refine the approach based on the feedback provided. The refined approach generated by Amazon Q meets our requirements, including unit tests, so we decide to click Proceed as shown in Figure 7 below. Figure 7: Confirm approach and click Proceed Now, Amazon Q will generate the code for implementation & create a PR with code changes that can be reviewed. Step 4: Review the PR Within our project, under Code on the left panel click on Pull requests. You should see the new PR created by Amazon Q. The PR description contains the approach that Amazon Q took to generate the code. This is helpful to reviewers who want to gain a high-level understanding of the changes included in the PR before diving into the details. You will also be able to review all changes made to the code as shown below in Figure 8. Figure 8: Changes within PR Step 5 (Optional): Provide feedback on PR After reviewing the changes in the PR, I leave comments on a few items that can be improved. Notably, all fields on the new input form for creating a new creature should be required. After I complete leaving comments, I hit the Create Revision button. Amazon Q will take my comments, update the code accordingly and create a new revision of the PR as shown in Figure 9 below. Figure 9: PR Revision created. After reviewing the latest revision created by Amazon Q, I am happy with the changes and proceed with testing the changes directly from CodeCatalyst by utilizing Dev Environments. Once I have completed testing of the new feature and everything works as expected, we will let our peers review the PR to provide feedback and approve the pull request. As part of following the steps in this blog post, if you upgraded your Space to Standard or Enterprise tier, please ensure you downgrade to the Free tier to avoid any unwanted additional charges. Additionally, delete the project and any associated resources deployed in the walkthrough. Unassign Amazon Q from any issues no longer being worked on. If Amazon Q has finished its work on an issue or could not find a solution, make sure to unassign Amazon Q to avoid reaching the maximum quota for generative AI features. For more information, see Managing generative AI features and Pricing. Best Practices for using Amazon Q Feature Development You can follow a few best practices to ensure you experience the best results when using Amazon Q feature development: When describing your feature or issue, provide as much context as possible to get the best result from Amazon Q. Being too vague or unclear may not produce ideal results for your use case. Changes and new features should be as focused as possible. You will likely not experience the best results when making large and complex changes in a single issue. Instead, break the changes or feature up into smaller, more manageable issues where you will see better results. Leverage the feedback feature to practice giving input on approaches Amazon Q takes to ensure it gets to a similar outcome as highlighted in the blog. Conclusion In this post, you’ve seen how you can quickly go from Idea to PR using the Amazon Q Feature development capability in CodeCatalyst. You can leverage this new feature to start building new features in your applications. Check out Amazon CodeCatalyst feature development today. About the authors Brent Everman Brent is a Senior Technical Account Manager with AWS, based out of Pittsburgh. He has over 17 years of experience working with enterprise and startup customers. He is passionate about improving the software development experience and specializes in AWS’ Next Generation Developer Experience services. Brendan Jenkins Brendan Jenkins is a Solutions Architect at Amazon Web Services (AWS) working with Enterprise AWS customers providing them with technical guidance and helping achieve their business goals. He has an area of specialization in DevOps and Machine Learning technology. Fahim Sajjad Fahim is a Solutions Architect at Amazon Web Services. He helps customers transform their business by helping in designing their cloud solutions and offering technical guidance. Fahim graduated from the University of Maryland, College Park with a degree in Computer Science. He has deep interested in AI and Machine learning. Fahim enjoys reading about new advancements in technology and hiking. Abdullah Khan Abdullah is a Solutions Architect at AWS. He attended the University of Maryland, Baltimore County where he earned a degree in Information Systems. Abdullah currently helps customers design and implement solutions on the AWS Cloud. He has a strong interest in artificial intelligence and machine learning. In his spare time, Abdullah enjoys hiking and listening to podcasts. View the full article

Today, AWS announces the AWS Project Development Kit (PDK) blueprints in Amazon CodeCatalyst. The AWS PDK provides building blocks for common patterns along with development tools to manage and build your projects. You can now use the AWS PDK in CodeCatalyst through the PDK blueprints, enabling you to compose one or more such blueprints together to create an application comprising of a React website, Smithy API, and the supporting CDK infrastructure to deploy the application to AWS. You can get started by creating a PDK backed project in CodeCatalyst. For more information, see the Blueprints documentation and PDK tutorial. View the full article

Today, AWS announces that CodeCatalyst Issues now support breaking down issues into smaller units called tasks. CodeCatalyst customers can now add up to 100 tasks within a single issue to further organize and plan out the work involved. Tasks can be added when creating a new issue or later added to existing issues and can be assigned to any of the project members. You can also reorder, mark as complete, or remove tasks on an issue. View the full article

Today, Amazon CodeCatalyst announces a new runtime docker image for customers to use with their build and test actions within workflows, along with the ability to choose between curated images. The new image contains updated tooling, including Node18. View the full article

Today, AWS announces the Bedrock GenAI chatbot blueprint in Amazon CodeCatalyst. CodeCatalyst customers can use this blueprint to quickly build and launch a generative AI chatbot with Amazon Bedrock and Anthropic’s Claude. This blueprint helps development teams build and deploy their own secure, login-protected LLM playground that can be customized to their data. You can get started by creating a project in CodeCatalyst. For more information, see the CodeCatalyst documentation and the Bedrock GenAI Chatbot documentation. View the full article

Today, we are excited to announce the availability of Amazon Q's feature development capability in preview, in Amazon CodeCatalyst. With this new capability, developers can assign a CodeCatalyst issue to Amazon Q, and Q performs the heavy lifting of converting a human prompt to an actionable plan, then completes code changes and a pull request that is assigned to the requester. Q will then monitor any associated workflows and attempt to correct any issues. The user can preview code changes and merge the pull request. Development teams can utilize this new capability as an end-to-end, streamlined experience within Amazon CodeCatalyst, without having to enter the IDE. View the full article

Today, I’m excited to introduce the preview of new generative artificial intelligence (AI) capabilities within Amazon CodeCatalyst that accelerate software delivery using Amazon Q. Accelerate feature development – The feature development capability in Amazon Q can help you accelerate the implementation of software development tasks such as adding comments and READMEs, refining issue descriptions, generating small classes and unit tests, and updating CodeCatalyst workflows — tedious and undifferentiated tasks that take up developers’ time. Developers can go from an idea in an issue to fully tested, merge-ready, running code with only natural language inputs, in just a few clicks. AI does the heavy lifting of converting the human prompt to an actionable plan, summarizing source code repositories, generating code, unit tests, and workflows, and summarizing any changes in a pull request which is assigned back to the developer. You can also provide feedback to Amazon Q directly on the published pull request and ask it to generate a new revision. If the code change falls short of expectations, you can create a development environment directly from the pull request, make any necessary adjustments manually, publish a new revision, and proceed with the merge upon approval. Example: make an API change in an existing application In the navigation pane, I choose Issues and then I choose Create issue. I give the issue the title, Change the get_all_mysfits() API to return mysfits sorted by the Age attribute. I then assign this issue to Amazon Q and choose Create issue. Amazon Q will automatically move the issue into the In progress state while it analyzes the issue title and description to formulate a potential solution approach. If there is already some discussion on the issue, it should be summarized in the description to help Q understand what needs to be done. As it works, Amazon Q will report on its progress by leaving comments on the issue at every stage. It will attempt to create a solution based on its understanding of the code already present in the repository and the approach it formulated. If Amazon Q is able to successfully generate a potential solution, it will create a branch and commit code to that branch. It will then create a pull request that will merge the changes into the default branch once approved. Once the pull request is published, Amazon Q will change the issue status to In Review so that you and your team know that the code is now ready for you to review. Summarize a change – Pull request authors can save time by asking Amazon Q to summarize the change they are publishing for review. Today pull request authors have to write the description manually or they may choose not to write it at all. If the author does not provide a description, it makes it harder for reviewers to understand what changes are being made and why, delaying the review process and slowing down software delivery. Pull request authors and reviewers can also save time by asking Amazon Q to summarize the comments left on the pull request. The summary is useful for the author because they can easily see common feedback themes. For the reviewers it is useful because they can quickly catch up on the conversation and feedback from themselves and other team members. The overall benefits are streamlined collaboration, accelerated review process, and faster software delivery. Join the preview Amazon Q is available in Amazon CodeCatalyst today for spaces in AWS Region US West (Oregon). Learn more Amazon CodeCatalyst product page Amazon CodeCatalyst User Guide Read more about Amazon Q Introducing Amazon Q, a new generative AI-powered assistant (preview) Amazon Q brings generative AI-powered assistance to IT pros and developers (preview) Upgrade your Java applications with Amazon Q Code Transformation (preview) New generative AI features in Amazon Connect, including Amazon Q, facilitate improved contact center service New Amazon Q in QuickSight uses generative AI assistance for quicker, easier data insights (preview) — Irshad View the full article

Today, AWS announces 4 new user roles available in Amazon CodeCatalyst: space Power user, space Limited access, project Reviewer, and project Read only. The Power user role has permissions to create projects and add AWS accounts. The space Limited access role is the default role for space members and has permissions to list space projects. The project Reviewer role has permission to use CodeCatalyst issues and approve pull requests, but it does not have source code or workflow change permissions. The Read only role gives read only access to project resources, with no create, update or delete permissions. View the full article

The entire AWS News Blog team is fully focused on writing posts to announce the new services and features during our annual customer conference in Las Vegas, AWS re:Invent! And while we prepare content for you to read, our services teams continue to innovate. Here is my summary of last week’s launches. Last week’s launches Here are some of the launches that captured my attention: Amazon CodeCatalyst – You can now add a cron expression to trigger a CI/CD workflow, providing a way to start workflows at set times. CodeCatalyst is a unified development service that integrates a project’s collaboration tools, CI/CD pipelines, and development and deployment environments. Amazon Route53 – You can now route your customer’s traffic to their closest AWS Local Zones to improve application performance for latency-sensitive workloads. Learn more about geoproximity routing in the Route53 documentation. Amazon RDS – The root certificates we use to sign your databases’ TLS certificates will expire in 2024. You must generate new certificates for your databases before the expiration date. This blog post details the procedure step by step. The new root certificates we generated are valid for the next 40 years for RSA2048 and 100 years for the RSA4098 and ECC384. It is likely this is the last time in your professional career that you are obliged to renew your database certificates for AWS. Amazon MSK – Replicating Kafka clusters at scale is difficult and often involves managing the infrastructure and the replication solution by yourself. We launched Amazon MSK Replicator, a fully managed replication solution for your Kafka clusters, in the same or across multiple AWS Regions. Amazon CodeWhisperer – We launched a preview for an upcoming capability of Amazon CodeWhisperer Professional. You can now train CodeWhisperer on your private code base. It allows you to give your organization’s developers more relevant suggestions to better assist them in their day-to-day coding against your organization’s private libraries and frameworks. Amazon EC2 – The seventh generation of memory-optimized EC2 instances is available (R7i). These instances use the 4th Generation Intel Xeon Scalable Processors (Sapphire Rapids). This family of instances provides up to 192 vCPU and 1,536 GB of memory. They are well-suited for memory-intensive applications such as in-memory databases or caches. X in Y – We launched existing services and instance types in additional Regions: Amazon Bedrock is now available in Europe (Frankfurt). This is important for customers in Europe because they often have to ensure their data stays in the European Union. You can now embed generative AI functionalities and access to large language models in your applications with the assurance that the prompts and customizations will stay in Europe. Amazon EC2 extended its footprint for multiple families of instances: m6gd instances are now available in Canada (Central) and South America (São Paulo), c6a in Canada (Central), m6a in Canada (Central) and Europe (Milan), and r6a instances in US West (N. California) and Asia Pacific (Singapore). Finally, m6id instances are now available in Europe (Zurich). Amazon EMR managed scaling is now available in Asia Pacific (Jakarta). Other AWS news Here are some other blog posts and news items that you might like: The Community.AWS blog has new posts to teach you how to integrate Amazon Bedrock inside your Java and Go applications, and my colleague Brooke wrote a survival guide for re:Invent first-timers. The Official AWS Podcast – Listen each week for updates on the latest AWS news and deep dives into exciting use cases. There are also official AWS podcasts in several languages. Check out the ones in French, German, Italian, and Spanish. Some other great sources of AWS news include: AWS Open Source Newsletter AWS Graviton Weekly AWS Cloud Security Weekly Last Week in AWS Upcoming AWS events Check your calendars and sign up for these AWS events: AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: Jaipur (November 4), Vadodara (November 4), and Brasil (November 4). AWS Innovate: Every Application Edition – Join our free online conference to explore cutting-edge ways to enhance security and reliability, optimize performance on a budget, speed up application development, and revolutionize your applications with generative AI. Register for AWS Innovate Online Asia Pacific & Japan on October 26. AWS re:Invent (November 27 – December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the re:Invent highlights for generative AI. You can browse all upcoming in-person and virtual events. And that’s all for me today. I’ll go back writing my re:Invent blog posts. Check back next Monday for another Weekly Roundup! -- seb This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS! View the full article

Today, Amazon Web Services announces support for scheduling Amazon CodeCatalyst workflows to be run at predetermined times. A workflow is an automated procedure that describes how to build, test, and deploy your code as part of a continuous integration and continuous delivery (CI/CD) system. A workflow defines a series of steps, or actions, to take during a workflow run. This launch allows you to add a cron expression to a workflow, providing a way to start workflows at set times. For example, you could schedule a workflow run to start every day at midnight. View the full article

Starting today, you can use Amazon CodeCatalyst Dev Environments (Preview) with linked GitHub repositories. CodeCatalyst is a unified software development service that makes it faster to build and deliver software on AWS. Dev Environments, a feature of CodeCatalyst, are preconfigured, scalable cloud development environments accessible from popular IDEs. They help developers instantly start editing, testing and pushing code. View the full article