AWS DevOps & Developer Productivity Blog

Modernize your Java application with Amazon Q Developer

Many organizations have critical legacy Java applications that are increasingly difficult to maintain. Modernizing these applications is a necessary, daunting, and risky task that takes the focus off of creating new value or features. This includes undocumented code, outdated frameworks and libraries, security vulnerabilities, a lack of logging and error handling, and a lack of input validation. Amazon Q Developer simplifies and accelerates the modernization of existing Java applications. It can analyze code to highlight areas for potential improvements, assist with resolving technical debt, suggest code optimizations, and facilitate the transition to current frameworks and libraries.

This blog post explores how to modernize legacy Java applications using Amazon Q Developer. We will take an example of Unicorn Store API, a Java application with Java 8 running on Amazon Elastic Compute Cloud (Amazon EC2). First, we will upgrade the underlying runtime from Java 8 to Java 17 and other common dependencies, including Spring. Then, we will reduce technical debt within the code by improving modularity and logging. Finally, we will redeploy this application in a container image using a modern computing option, AWS Fargate.

The Unicorn Store API provides CRUD operations to manage Unicorn records in a database. It is built with Maven.

You will follow the below steps to modernize this application and bring it to Fargate using Amazon Q Developer.

  1. Upgrade the application to Java 17 to leverage the latest features.
  2. Reduce existing technical debt in the codebase.
  3. Make the application cloud native and deploy it to AWS.

In this walkthrough, we are using IntelliJ IDEA IDE with the latest version of Amazon Q Developer plugin for IntelliJ IDEA

Upgrade from Java 8 to Java 17

Outdated applications require increased effort to maintain security and stability. As a developer, you must continually relearn framework changes and optimizations that others have discovered in previous upgrades. The effort required to maintain the application makes it difficult to balance necessary updates with adding new features.

With Amazon Q Developer agent for code transformation, you can keep applications updated and supported in just a few steps. This removes vulnerabilities from unsupported versions, improves performance, and frees up time to focus on adding new features. Amazon Q Developer agent for code transformation accelerates application maintenance, upgrades, and migration in minutes. It enables developers to remove much of the undifferentiated work out of the tedious task of maintaining, upgrading and migrating existing application workloads, saving up to days’ or months’ worth of the undifferentiated work involved in moving from older language versions.

Let’s upgrade our Unicorn Store API from Java 8 to Java 17 using Amazon Q Developer agent for code transformation to leverage the latest features and optimization. In IntelliJ IDE, you enter /transform in the Amazon Q chat panel and provide the necessary details for Amazon Q Developer to start upgrading the project.

IntelliJ IDE showing the Amazon Q chat panel open with the /transform command entered and details provided about the project to upgrade. This triggers the Amazon Q Developer agent for code transformation to analyze the code, generate a transformation plan, and upgrade frameworks and libraries like Spring, Spring Boot, JUnit, and Log4j to be compatible with Java 17.

Amazon Q Developer agent for code transformation automatically analyzes the existing code, generates a transformation plan, and completes the transformation tasks suggested by the plan. While doing so, it upgrades popular libraries and frameworks to a version compatible with Java 17, including Spring, Spring Boot, JUnit, JakartaEE, Mockito, Hibernate, and Log4j to their latest available major versions. It also updates deprecated code components according to Java 17 recommendations. To start with the Amazon Q Developer agent for code transformation capability, you can read and follow the steps at Upgrade language versions with Amazon Q Developer agent for Code Transformation.

Once complete, you can review the transformed code, complete with build and test results, before accepting the changes.

In the IntelliJ IDE, Amazon Q Developer agent for code transformation summarizes all the changes it made after the transformation of the current project to Java 17 is complete. User clicks on the View diff button. The list of all the files that have been modified or added is displayed. User has the option to review the diff, and then accept or reject them.

Reduce technical debt in the codebase

Technical debt accumulates in any codebase over time. Some technical debt may be unavoidable to meet deadlines, but must be tracked and prioritized to pay back later. If left unmanaged, compounding technical debt will make development slower and expensive. Reducing technical debt should be an ongoing team effort, but often falls behind other priorities. Amazon Q Developer streamlines modernizing legacy Java code by identifying and remediating technical debt. Amazon Q Developer reduces the time and resources it takes to analyze the code by providing a list of issues that contribute to technical debt in a codebase. This makes it easy for software development teams to prioritize technical debt items and make informed decisions about which technical debt to address first.

Let’s find the list of technical debt in our Unicorn Store API. In IntelliJ IDE, use Send to Prompt option to send the highlighted code to the Amazon Q chat panel and prompt to provide a list of all technical debt. Amazon Q Developer lists all technical debt in detail.

In the IntelliJ IDE, user has opened the code for one of the classes (UnicornController.java in this case). User right-clicks inside this file. This opens a context window. User selects Amazon Q, then selects Send to Prompt. The code is now displayed in the Amazon Q Chat panel. The user enters this message in the chat window: “Analyze the selected code and list all technical debt”. Amazon Q Developer generates a list of all potential technical debt or areas of improvement. It explains the impact of each technical debt.

Once you identify the technical debt, the next step is to gradually remediate them. Amazon Q Developer reduces the time it takes to implement the code to remediate the technical debt. As a developer, you can interact with Amazon Q Developer agent for software development within your IDE to get help with code suggestions for a specific task that you are trying to accomplish. It uses the code in whole project as context and provides an implementation plan that includes code updates it plans to make across all the files in the project. You can review the plan, and once you are satisfied with the plan, you can ask Amazon Q Developer to generate the code based on the proposed plan. This saves developers’ effort compared to manual updates.

For the technical debt identified for Unicorn Store API in the above step, let’s use Amazon Q Developer to address the missing logging technical debt. In IntelliJ IDE, enter /dev in the Amazon Q chat panel with the details on the logging technical debt. Amazon Q Developer generates an implementation plan and code to add logging based on the full project context. To get started with Amazon Q Developer agent for software development, you can refer to the steps at Develop software with the Amazon Q Developer agent for software development.

In the IntelliJ IDE, the user opens Amazon Q Chat panel, enters /dev and provides input about the task detail “Implement logging for critical REST endpoints. Log relevant error details such as request parameters and stack traces to facilitate debugging and troubleshooting”. The Amazon Q Developer agent for software development generates the steps to implement this task, including changes that will be made to existing files and any new files that will be created. User reviews all the changes, then clicks on Generate code button. Amazon Q generates a list of code suggestions. User clicks on a file from the list, and reviews the diff between the existing code in that file, and the new code generated by Amazon Q Developer. User then clicks on Insert Code button.

Modernizing legacy Java code requires continuous refactoring to incrementally enhance quality and avoid accumulating technical debt over time. Amazon Q Developer simplifies this iterative process through its Refactor capability. Amazon Q Developer provides a refactored version of the selected code, alongside explanations of each change and its coding benefit. It helps you to understand the changes by explaining each change and the benefit of making the change in the existing code. You can read further about this capability at Explain and update code with Amazon Q Developer.

Let’s leverage this feature to refine methods in the UnicornController class in our Unicorn Store API project. Amazon Q Developer furnishes the updated code with better code readability or efficiency, among other improvements, for you to review.

In the IntelliJ IDE, user highlights a section of code (createUnicorn and updateUnicorn methods in the UnicornController class in this case), and right-clicks on the highlighted code. This opens a context window. User selects Amazon Q, and then selects Refactor code. The code is now displayed in the Amazon Q Chat panel. Amazon Q Developer generates a refactored version of the selected code. It also provides a list of all the changes made and the benefit of changing the code.

Make the application cloud native and deploy to AWS

The final step in the modernization journey is to make the application cloud-native and deploy to AWS. Cloud native is the software approach of building, deploying, and managing modern applications in cloud computing environments. These cloud native technologies support fast and frequent changes to applications without impacting service delivery, providing adopters with an innovative, competitive advantage. Let’s see how Amazon Q Developer can assist in making our Unicorn Store API project cloud native.

In IntelliJ IDE, open the Amazon Q Chat, and prompt Amazon Q Developer to provide a recommended approach to make the project cloud native and deploy to AWS.

In the IntelliJ IDE, user sends the content of the pom.xml file to Amazon Q Chat window, and asks Amazon Q Developer “What is the recommended way to make this micro service, cloud native and deploy to AWS?” Amazon Q generates the steps and a high-level implementation guide. In this case, it includes suggestions to containerize the application, use Amazon Elastic Container Registry, deploy to Amazon Elastic Container Service (ECS), implement a load balancer, leverage AWS Fargate, implement monitoring and logging, implement CI/CD, leverage other AWS managed services, implement security best practices, monitor and optimize the deployment.

Amazon Q Developer analyzes the code and details the steps involved in making this application cloud native. The detailed steps involve containerizing the application, deploying the container application to AWS services such as Amazon Elastic Container Service (Amazon ECS), Fargate for running containers in a serverless manner, Amazon Elastic Container Registry (Amazon ECR) for pushing the container image, accessing the application through AWS Application Load Balancer (ALB), Amazon CloudWatch for monitoring and associated services like Amazon Virtual Private Cloud (VPC) and Subnets.

Let’s ask Amazon Q Developer to implement the steps outlined in the previous chat conversation. First, ask Amazon Q Developer to create a docker file to containerize the application. The containerization process streamlines application development by decoupling the software from the underlying hardware and other dependencies. This approach enhances speed, efficiency, and security by isolating different components within the containerized environment.

In the IntelliJ IDE, user continues the conversation in the previous chat window. User asks Amazon Q Developer to provide implementation for the step 1 (containerize the application) and create a Dockefile for the application. Amazon Q generates a Dockerfile that includes all the necessary steps. User creates a new file named Dockerfile, and copies the contents from the chat window to the new file. Next, user opens the terminal within the IntelliJ IDE. User copies the build and run commands from the chat and pastes it in the terminal window. The application successfully builds in the docker container. Finally, application runs successfully as a docker container.

Having successfully developed a container-based application, let’s leverage Amazon Q Developer’s capabilities to generate an AWS CloudFormation template. This template will enable us to deploy the required resources to AWS using Infrastructure as Code (IaC). IaC allows us to programmatically provision and manage our computing infrastructure, eliminating the need for manual processes and configurations. Manual infrastructure management can be time-consuming and error-prone, especially when dealing with large-scale applications.

To facilitate the creation of the CloudFormation template, let’s revisit the suggestions from our previous conversation and compile a list of the resources that need to be provisioned in AWS. Once you have this list, you can ask Amazon Q Developer to generate the CloudFormation template based on these resource requirements.

In the IntelliJ IDE, user copies the steps outlined in the previous conversation, and asks Amazon Q Developer to create a CloudFormation template to deploy this application to AWS. Amazon Q Developer creates the full CloudFormation template to deploy all resources. User creates a new YAML file, and copies the contents from the chat window to the new file. The generated CloudFormation contains the steps to deploy VPCs, subnets, ECR, ECS cluster, Tasks, LoadBalancer, CloudWatch logs, etc. User reviews the generated code for accuracy before deployment.

Amazon Q Developer can generate the CloudFormation template with all the required resources as outlined in the steps to deploy the container in AWS in a secure, reliable, and scalable manner.

Now that we have the CloudFormation template, once CloudFormation is deployed, let’s push the local docker image of our Unicorn Store API to Amazon ECR and start the Fargate tasks required to run the application in AWS.

In the IntelliJ IDE, user runs the commands to push the docker image of the application created using Amazon Q Developer.

In this way, you can use Amazon Q Developer to make your application cloud native by designing the steps to deploy to the cloud, helping migrate your application to container-based solution and even writes Infrastructure as code scripts to deploy your application to AWS.

Conclusion

Amazon Q Developer empowers developers to simplify and accelerate the modernization of legacy Java applications. By leveraging Amazon Q Developer, developers can bring outdated applications up to current frameworks and deploy them to AWS in a cloud-native architecture. This streamlines the process, reducing the effort, risk, and maintenance required. Developers save significant time and resources, which can now be used to focus on building new features and enhancing modernized applications rather than managing technical debt.

To learn more about Amazon Q Developer, see the following resources:

Chetan Makvana

Chetan Makvana is a Senior Solutions Architect with Amazon Web Services. He works with AWS partners and customers to provide them with architectural guidance for building scalable architecture and implementing strategies to drive adoption of AWS services. He is a technology enthusiast and a builder with a core area of interest on generative AI, serverless, and DevOps. Outside of work, he enjoys watching shows, traveling, and music.

Venugopalan Vasudevan

Venugopalan Vasudevan is a Senior Specialist Solutions Architect at Amazon Web Services (AWS), where he specializes in AWS Generative AI services. His expertise lies in helping customers leverage cutting-edge services like Amazon Q, and Amazon Bedrock to streamline development processes, accelerate innovation, and drive digital transformation. Venugopalan is dedicated to facilitating the Next Generation Developer experience, enabling developers to work more efficiently and creatively through the integration of Generative AI into their workflows.

Surabhi Tandon

Surabhi Tandon is a Senior Technical Account Manager at Amazon Web Services (AWS). She supports enterprise customers achieve operational excellence and help them with their cloud journey on AWS by providing strategic technical guidance. Surabhi is a builder with interest in Generative AI, automation, and DevOps. Outside of work, she enjoys hiking, reading and spending time with family and friends.