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Kubernetes is an open-source platform for automating the deployment, scaling, and management of containerized applications. It is like the operating system of the cloud. A Kubernetes cluster comprises a control plane (or brain) and worker nodes that run your workloads. Here we’ll discuss if it is worth starting the modernization journey with Kubernetes for legacy Java applications.

Start Your App Modernization Journey with Kubernetes for Legacy Java Applications

We can look at how organizations have traditionally deployed applications to understand how Kubernetes is useful.

In earlier days, organizations ran their applications directly on physical servers. This resulted in resource contention and conflicts between applications. One application could monopolize a system resource (CPU, memory, or network bandwidth), starving other applications. So, the performance of those applications would suffer.

One solution to this problem was to run each application on a separate server. But this approach had two disadvantages – underutilization of compute resources and escalating server costs.

Another solution was virtualization. Virtualization involves creating Virtual Machines (VMs). A VM is a virtual computer that is allocated a set of physical resources on the host system, including its own operating system.

A VM runs only one application. Several VMs can run on a server. The VMs isolate applications from each other. Virtualization offers scalability, as one can add or remove VMs when needed. There is also a high utilization of server resources. Hence, it is an excellent solution and is still popular.

Next, containers appeared on the market, the most popular of which is Docker. Containers are like VMs, but share an operating system with other containers. Hence, they are comparatively lightweight. A container is independent of the underlying infrastructure (server). It is portable across different clouds and operating systems. So, containers are a convenient way to package and deploy applications.

In a production environment, engineers must manage the containers running their apps. They must add containers to scale up and replace or restart a container that has gone down. They must regularly upgrade the application. If all this could be done automatically, life would be easier, especially when dealing with thousands or millions of containers.

This is where Kubernetes comes in. It provides a framework that can run containers reliably and resiliently. It automates the deployment, scaling, upgrading, backup and restoration, and general management of containers. Google, the developer of Kubernetes, has used it to deploy millions of containers per hour. In the past few years, Kubernetes has become the number one container management platform.

What is a Kubernetes Operator?

Managing stateful applications running on Kubernetes is difficult. The Kubernetes Operator helps handle such apps. A Kubernetes Operator is an automated method that packages, maintains, and runs a stateful Kubernetes application. The Operator uses Kubernetes APIs to manage the lifecycle of the software it controls.

An Operator can manage a cluster of servers. It knows the configuration details of the applications running on these servers. So, it can create the cluster and deploy the applications. It can monitor and manage the applications, update them with newer versions, and automatically restart them if they fail. The Operator can take regular backups of the application data.

In short, the Kubernetes Operator replaces a human operator who would otherwise have performed these tasks.

How Should You Run Kubernetes?

There are many options for running Kubernetes. Keep in mind that you will not just need to set up the Kubernetes clusters one time, but you’ll also need to make frequent changes and upgrades.

• Host and Manage Your Clusters: You do everything yourself. This will require a significant investment of time and effort.
• Start with Managed Services: The provider installs and runs Kubernetes on their servers and takes care of all administrative overheads. You get a fully working, production-grade cluster in minutes. It will only cost you a few dollars a day. There are many good options: Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), and IBM Cloud Kubernetes Service.
•  Kubernetes Installers: This is a suitable option if you want to self-host Kubernetes. Installer tools and services help you set up and manage your Kubernetes clusters. Popular tools include kOps, Kubeadm, kubespray, Rancher Kubernetes Engine (RKE), and Puppet Kubernetes Module.
• Multi-Cloud Kubernetes Clusters: There are tools available to manage the workload for organizations that deploy apps to the cloud. Popular tools are VMWare Tanzu Mission Control, Red Hat OpenShift, and Google Anthos.

Adopting Kubernetes for Legacy Java Technologies

Let’s look at how using Kubernetes (or Kubernetes Operators) alone, instead of completely modernizing your applications, makes it easier to work with many traditional Java frameworks, application servers, and databases.

Kubernetes for Java Frameworks

Spring Boot, Quarkus, and Micronaut are popular frameworks for working with Java EE applications in a modern way.

Using Spring Boot with Kubernetes

Spring Framework is a popular, open-source, enterprise-grade framework for creating standalone, production-ready applications which run on the Java Virtual Machine (JVM). Spring Boot is a tool that uses Spring to build web applications and microservices quickly and easily. It allows you to create a Spring app effortlessly.

Deploying a Spring Boot application to Kubernetes involves a few simple steps:

1.   Create a Kubernetes cluster, either locally or on a cloud provider.

2.   If you have a Spring Boot application, clone it in the terminal. Otherwise, create a new application. Make sure that the application has some HTTP endpoints.

3.   Build the application. A successful build results in a JAR file.

4.   Containerize the application in Docker using Maven, Gradle, or your favorite tool.

5.   You need a YAML specification file to run the containerized app in Kubernetes.  Create the YAML manually or using the kubectl command.

6.   Now deploy the app on Kubernetes, again using kubectl.

7.   You can check whether the app is running by invoking the HTTP endpoints using curl.

Check out the Spring documentation for a complete set of the commands needed.

Using Quarkus with Kubernetes:

Quarkus aims to combine the benefits of the feature-rich, mature Java ecosystem with the operational advantages of Kubernetes. Quarkus auto-generates Kubernetes resources based on defaults and user-supplied configuration for Kubernetes, OpenShift, and Knative. It creates the resource files using Dekorate (a tool that generates Kubernetes manifests).

Quarkus then deploys the application to a target Kubernetes cluster by applying the generated manifests to the target cluster’s API Server. Finally, Quarkus can create a container image and save it before deploying the application to the target platform.

The following steps describe how to deploy a Quarkus application to a Kubernetes cluster on Azure. The steps for deploying it on other cloud platforms are similar.

1.       Create a Kubernetes cluster on Azure.

2.       Install the Kubernetes CLI on your local computer.

3.       From the CLI, connect to the cluster using kubectl.

4.      Azure expects web applications to run on Port 80. Update the Dockerfile.native file to reflect this.

5.      Rebuild the Docker image.

6.      Install the Azure Command Line Interface.

7.      Either deploy the container image to a Kubernetes cluster, or

8.     Deploy the container image to Azure App Service on Linux Containers (this option provides scalability, load-balancing, monitoring, logging, and other services).

Quarkus includes a Kubernetes Client extension that enables it to unlock the power of Kubernetes Operators.

Using Kubernetes with Micronaut

Micronaut is a modern, full-stack Java framework that supports the Java, Kotlin, and Groovy languages. It tries to improve over other popular frameworks, like Spring and Spring Boot, with a fast startup time, reduced memory footprint, and easy creation of unit tests.

The Micronaut Kubernetes project simplifies the integration between the two by offering the following facilities:

• It contains a Service Discovery module that allows Micronaut clients to discover Kubernetes services.
• The Configuration module can read Kubernetes’ ConfigMaps and Secrets instances and make them available as PropertySources in the Micronaut application. Then any bean can read the configuration values using @Value (or any other method). The Configuration module will monitor changes in the ConfigMaps, propagate them to the Environment, and refresh it. So, these changes will be available immediately in the application without a restart. 
• The Configuration module also provides a KubernetesHealthIndicator that provides all kinds of information about the pod in which the application is running. 
• Overall, the library makes it easy to deploy and manage Micronaut applications on a Kubernetes cluster.

Kubernetes for Legacy Java Application Servers

Java Application Servers are web servers that host Java EE applications. They provide Java EE specified services like security, transaction support, load balancing, and managing distributed systems.

Popular Java EE compliant application servers include Apache Tomcat, Red Hat JBoss and Wildfly, Oracle WebLogic, and IBM WebSphere. Businesses have been using them for years to host their legacy Java applications. Let’s see how you can use them with Kubernetes.

Using Apache Tomcat with Kubernetes

Here are the steps to install and configure Java Tomcat applications using Kubernetes:

1.       Build the Tomcat Operator with the source code from Github.

2.       Push the image to Docker.

3.       Deploy the Operator image to a Red Hat OpenShift cluster.

4.       Now deploy your application using the custom resources operator.

  You can also deploy an existing WAR file to the Kubernetes cluster.

Using Red Hat OpenShift / JBoss EAP / Wildfly with Kubernetes

Red Hat OpenShift is a Kubernetes platform that offers automated operations and streamlined lifecycle management. It helps operations teams provision, manage, and scale Kubernetes platforms. The platform can bundle all required components like libraries and runtimes and ship them as one package.

To deploy an application in Kubernetes, you must first create an image with all the required components and containerize it. The JBoss EAP Source-to-Image (S2I) builder tool creates these images from JBoss EAP applications. Then use the JBoss EAP Operator to deploy the container to OpenShift.

The JBoss EAP Operator simplifies operations while deploying applications. You only need to specify the image and the number of instances to deploy. It supports critical enterprise functionality like transaction recovery and EJB (Enterprise Java Beans) remote calls.

Some benefits of migrating JBoss EAP apps to OpenShift include reduced operational costs, improved resource utilization, and a better developer experience. In addition, you get the Kubernetes advantages in maintaining, running, and scaling application workloads.

Thus, using OpenShift simplifies legacy application development and deployment.

Using Oracle WebLogic with Kubernetes

Oracle’s WebLogic server runs some of the most mission-critical Java EE applications worldwide.

You can deploy the WebLogic server in self-hosted Kubernetes clusters or on Oracle Cloud. This combination offers the advantages of automation and portability. You can also easily customize multiple domains. The Oracle WebLogic Server Kubernetes Operator simplifies creating and managing WebLogic Servers in Kubernetes clusters.

The operator enables you to package your WebLogic Server installation and application into portable images. This, along with the resource description files, allows you to deploy them to any Kubernetes cluster where you have the operator installed.

The operator supports CI/CD processes. It facilitates the integration of changes when deploying to different environments, like test and production.

The operator uses Kubernetes APIs to perform provisioning, application versioning, lifecycle management, security, patching, and scaling.

Using IBM WebSphere with Kubernetes

IBM WebSphere Application Server is a flexible and secure Java server for enterprise applications. It provides integrated management and administrative tools, centralized logging, monitoring, and many other features.

IBM Cloud Pak for Applications is a containerized software solution for modernizing legacy applications. The Pak comes bundled with WebSphere and Red Hat OpenShift. It enables you to run your legacy applications in containers and deploy and manage them with Kubernetes.

Related: The Best Java Monolith Migration Tools

Kubernetes with Legacy Java Databases

For orchestrators like Kubernetes, managing stateless applications is a breeze. However, they find it challenging to create and manage stateful applications with databases. Here is where Operators come in.

In most organizations, Database Administrators create database clusters in the cloud and secure and scale them. They watch out for patches and upgrades and apply them manually. They are also responsible for taking backups, handling failures, and monitoring load and efficiency.

All this is tedious and expensive. But Kubernetes Operators can perform these tasks automatically without human involvement.

Let us look at how they help with two popular database platforms, MySQL and MongoDB.

Using MySQL with Kubernetes

Oracle has released the open-source Kubernetes Operator for MySQL. It is a Kubernetes Controller that you install inside a Kubernetes cluster. The MySQL Operator uses Customer Resource Definitions to extend the Kubernetes API. It watches the API server for Customer Resource Definitions relating to MySQL and acts on them. The operator makes running MySQL inside Kubernetes easy by abstracting complexity and reducing operational overhead. It manages the complete lifecycle with automated setup, maintenance, upgrades, and backup.

Here are some tasks that the operator can automate:

• Create and scale a self-healing MySQL InnoDB cluster from a YAML file
• Backup a database and archive it in object storage
•  List backups and fetch a particular backup
•  Backup databases according to a defined schedule

If you are planning to deploy MySQL inside Kubernetes, the MySQL Operator can do the heavy lifting for you.

Using MongoDB with Kubernetes Operator

MongoDB is an open-source, general-purpose, NoSQL (non-relational) database manager. Its data model allows users to store unstructured data. The database comes bundled with a rich set of APIs.

MongoDB is very popular with developers. However, manually managing MongoDB databases is time-consuming and difficult.

MongoDB Enterprise Kubernetes Operator: MongoDB has released the MongoDB Enterprise Operator. The operator enables users to deploy and manage database clusters from within Kubernetes. You can specify the actions to be taken in a declarative configuration file. Here are some things you can do with MongoDB using the Enterprise Operator:

• Deploy and scale MongoDB clusters of any size
• Specify cluster configurations like security settings, resilience, and resource limits
•  Enable centralized logging

Note that the Enterprise Operator performs all its activities using the OpsManager, the MongoDB management platform.

Running MongoDB using Kubernetes is much easier than doing it manually.

MongoDB comes in many flavors. The MongoDB Enterprise Operator supports the MongoDB Enterprise version, the Community Operator supports the Community version, and the Atlas Operator supports the cloud-based database-as-a-service Atlas version.

How do Kubernetes (and Docker) make a difference with legacy apps?

Using Kubernetes provides tactical modernization benefits but not strategic gains (re-hosting compared to refactoring). 

When legacy Java applications use Kubernetes (or Kubernetes Operators) with Docker, they immediately get some benefits. To recap, they are:

Improved security: Container platforms have security capabilities and processes baked in. One example is the concept of least privilege. It is easy to add additional security tools. Containers provide data protection facilities, like encrypted communication between containers, that apps can use right away.

Simplified DevOps: Deploying legacy apps to production is error-prone because the team must individually deploy executables, libraries, configuration files, and other dependencies. Failing to deploy even one of these dependencies or deploying an incorrect version can lead to problems.

But when using containers, developers build an image that includes the code and all other required components. Then they deploy these images in containers. So, nothing is ever left out. With Kubernetes, container deployment and management are automated, simplifying the DevOps process.

This approach has some drawbacks. There is no code-level modernization, and there are no architectural changes. The original monolith stays intact. There is no reduction of technical debt – the code remains untouched. There are no architectural changes. There is limited scalability – the entire application (now inside a container) has to be scaled. 

With modern applications, we can scale individual microservices. The drawbacks of working on a monolith created long ago with older technology versions,  are unexpected linkages and poorly understood code. Hence, there is no increase in development velocity.

Using Kubernetes is a Start, but What If You Want to Go Further?

Enterprises use containers to build applications faster, deploy to hybrid cloud environments, and scale automatically and efficiently. Container platforms like Docker provide a host of benefits. These include increased ease and reliability of deployment and enhanced security. Using Kubernetes (directly or with Operators) with containers makes the process even better.

We have seen that using Kubernetes with unchanged legacy applications has many advantages. But these advantages are tactical.

Consider a more transformational form of application modernization to get the significant strategic advantages that will keep your business competitive. This would include breaking up legacy Java applications into microservices, creating CI/CD pipelines for deployment, and moving to the cloud. In short, it involves making your applications cloud-native.Carrying out such a full-scale modernization can be risky. There are many options to consider and many choices to make. It involves a lot of work. You’ll want some help with that.

vFunction has created a repeatable platform that can transform legacy applications to cloud-native, quickly, safely, and reliably. Request a demo to see how we can make your transformation happen.

Refactoring is a popular and effective way of modernizing legacy applications. However, to get the maximum benefits of modernization, we should not stop after refactoring. Instead, we should continue modernization after refactoring as part of a process of Continuous Modernization, a term coined by a leading cloud modernization platform.

Continuous Modernization: Modernization after Refactoring

Businesses constantly adapt and improve to handle new opportunities and threats. Similarly, they must also continuously keep upgrading their enterprise software applications. With time, all enterprise applications are susceptible to technical debt accumulation. Often, the only way to repay the debt is to refactor and move to the cloud. This process of application modernization provides significant benefits.

A “megalith” is a large traditional monolithic application that has over 5 million lines of code and 5,000 classes. Companies that maintain megaliths often choose the less risky approach of incremental modernization. So, at a point in time, part of their application may have been modernized to microservices running in the cloud and deployed by CI/CD pipelines. The remaining portion of the legacy app remains untouched. 

Modernization is an Ongoing Process

Three of the most popular approaches to modernization are rehosting, re-platforming, and refactoring.

Rehosting (or Lift and Shift): This involves moving applications to the cloud as-is or with minimal changes. Essentially, you change the place where the application runs. Often, this means migrating your application to the cloud. However, you can move it to shared servers, a private cloud, or a public cloud. 

Re-platforming: The approach takes a newer runtime platform, and inserts the old functionality. You’ll end up with a mosaic that mixes the old in with the new. From the end user’s perspective, the program operates the same way it was before modernization, so they don’t need to learn much in the way of new features. At the same time, your legacy application will run faster than before and be easier to update or repair.

Refactoring:  Refactoring is the process of reorganizing and optimizing existing code. It lets you get rid of outdated code, reduce significant technical debt, and improve non-functional attributes such as performance, security, and usability. By refactoring, you can also adapt to changing requirements since cloud-native, and microservice architectures make it possible for applications to add new features or modify existing ones right away.

Of these, refactoring requires the most effort and yields the most benefits. In addition to code changes, refactoring also includes process-related enhancements like CI/CD to unleash the full power of modernization. Modernization, however, is not a once-and-done activity. In fact, modernization after refactoring is a continuous process.

The Role of DevOps in Modernization

Application modernization and DevOps go hand in hand. DevOps (Development + Operations) is a set of processes, practices, and tools that enable an organization to deliver applications and updates at high velocity. DevOps facilitates previously siloed groups – developers and operations – to coordinate and produce better products.

Continuous integration (CI) and continuous delivery/deployment (CD) are the two central tenets of DevOps. For maximum benefits, modernization after refactoring should include CI and CD.

Continuous Integration: Overview, History, and How It Works

Software engineers work on “branches,” which are private copies of code only they can access. They make the copies from a central code repository, often called a “mainline” or “trunk”. After making changes to their branch and testing, they must “merge” (integrate) their changes back into the central repository. This process could fail if, in the meantime, another developer has also changed the same files. Here, a “merge conflict” results and must be resolved, often a laborious process.

Continuous integration (CI) is a DevOps practice in which software developers frequently merge their code changes into the central depository. Because developers check in code very often, there are minimal merge conflicts. Each merge triggers an automated build and test cycle. Developers fix all problems immediately. CI’s goals are to reduce integration issues, find and resolve bugs sooner, and release software updates faster.

Grady Booch first used the phrase Continuous Integration in his book, “Object-Oriented Analysis and Design with Applications”, in 1994. When Kent Beck proposed the Extreme Programming development process, he included twelve programming practices he felt were essential for developing quality software. Continuous integration was one of them.

How Does Continuous Integration Work?

There are several prerequisites and requirements for adopting CI.

Maintain One Central Source Code Repository

A central source code repository (or repo) under a version control system is a prerequisite for Continuous Integration. When a developer works on the application, they check out the latest code from the repo. After making changes, they merge their changes back to the repo. So, the repo contains the latest, or close to the latest, code at all times.

Automated Build Process

It should be possible to kick off the build with a single command. The build process should do everything – generate the executables, libraries, databases, and anything else needed –to get the system up and running.

Automated Testing

Include automated tests in the build process. The test suite should verify most, if not all, of the functionality in the build. A report should tell you how many tests passed at the end of the test run. If any test fails, the system should mark the build as failed, i.e., unusable.

A Practice of Frequent Code Commits

As mentioned earlier, a key goal of CI is to find and fix merge problems as early as possible. Therefore, developers must merge their changes to the mainline at least once a day. This way, merge issues don’t go undetected for more than a day at the most.

Every Commit Should Trigger a Build

Every code commit should trigger a build on an integration machine. The commit is a success only if the resulting build completes and all tests pass. The developer should monitor the build, and fix any failures immediately. This practice ensures that the mainline is always in a healthy state.

Fast Build Times

The build time is the time taken to complete the build and run all tests. What is an acceptable build time? Developers commit code to the mainline several times every day. The last thing they want to do after committing is to sit around twiddling their thumbs. Approximately 10 minutes is usually acceptable.

Fix Build Breaks Immediately

A goal of CI is to have a release-quality mainline at all times. So, if a commit breaks the build, the goal is not being met. The developer must fix the issue immediately. An easy way to do this is to revert the commit. Also, the team should consciously prioritize the correction of a broken build as a high-priority task. Team members should be careful to only check in tested code.

The Integration Test Environment Should Mirror the Production Environment

The goal of testing is to discover any potential issues that may appear in production before deployment. So, the test environment must be as similar to the production environment as possible. Every difference adds to the risk of defects escaping to production.

Related: Succeed with an Application Modernization Roadmap

Continuous Delivery/Deployment

CD stands for both continuous delivery and continuous deployment. They differ only in the degree of automation.

Continuous delivery is the next step after continuous integration. The pipeline automatically builds the newly integrated code, tests the build, and keeps the deployment packages ready. Manual intervention is needed to deploy the build to a testing or production environment.

In continuous deployment, the entire process is automated. Every successful code commit results in deploying a new version of the application to production without human involvement.

CI streamlines the code integration process, while CD automates application delivery.

Popular CI/CD Tools

There are many CI/CD tools available. Here are the leading ones.

Jenkins

Jenkins is arguably the most popular CI/CD tool today. It is open-source, free, and supports almost all languages and operating systems. Moreover, it comes with hundreds of plugins that make it easy to automate any building, testing, or deployment task.

AWS CodeBuild

CodeBuild is a CI/CD tool that compiles code, runs tests, and generates ready-to-deploy software packages. It takes care of provisioning, managing, and building your build servers. CodeBuild automatically scales and runs concurrent builds. It comes with an IDE (Integrated Development Environment).

GitLab

GitLab is another powerful CI/CD tool. An interesting feature is its ability to show performance metrics of all deployed applications. A pipeline graph feature shows the status of every task. GitLab makes it easy to manage Git repositories. It also comes with an IDE.

GoCD

GoCD from ThoughtWorks is a mature CI/CD tool. It is free and open-source. GoCD visually shows the complete path from check-in to deployment, making it easy to analyze and optimize the process. This tool has an active user community.

CircleCI

CircleCI is one of the world’s largest CI/CD platforms. The simple UI makes it easy to set up projects. It integrates smoothly with Github and Bitbucket. You can conveniently identify failing tests from the UI. It has a free tier of service that you can try out before committing to the paid version.

You should select the CI/CD tool that helps you optimize your software development process.

Related Cloud vs Cloud-Native: Taking Legacy Java Apps to the Next Level

The Benefits of CI and CD

The complete automation of releases — from compiling to testing to the final deployment — is a significant benefit of the CI/CD pipeline. Other benefits of the CI/CD process include:

• Reduction of deployment time: Automated testing makes the development process very efficient and reduces the length of the software delivery process. It also improves quality.
• Increase in agility: Continuous deployment allows a developer’s changes to the application to go live within minutes of making them.
• Saving time and money: Automation results in fast development, testing, and deployment. The saving in time translates to a cost-saving. More time is available for innovation. Code reviewers save time because they can now focus on code instead of functionality.
• Continuous feedback loop: The CI/CD pipeline is a continuous cycle of building, testing, and deployment. Every time the tests run and find issues, developers can quickly take corrective action, resulting in continuous improvement of the product.
• Address issues earlier in the cycle: Developers commit code frequently, so merge conflicts surface early. Every check-in generates a build. The automated test suite runs on each build, so the team catches integration issues quickly.
• Testing in a production-like environment: You mitigate risks by setting up a production environment clone for testing.
• Improving team responsiveness: Everyone on the team can change code, respond to feedback, and respond promptly to any issues.

These are some notable benefits of modernization.

CI and CD: differences

There are fundamental differences between continuous integration and continuous deployment.

For one, CI happens more frequently than CD.

CI is the process of automating the build and testing code changes. CD is the process of automating the release of code changes.

CI is the practice of merging all developer code to the mainline several times a day. CD is the practice of automatically building the changed code and testing and deploying it to production.

Continuous Modernization after Refactoring

We started this article by stating that application modernization is often the only way software teams can pay off their technical debt. We also mentioned continuous modernization. Companies are increasingly leaning toward continuous modernization. They constantly monitor technical debt, make sure they have no dead code, and ensure good test coverage. Their goal is to prevent the modernized code from regressing.  

How to Build Continuous Modernization Into Your CI/CD Pipeline

We have seen the many benefits that CI/CD provides. As more and more companies realize the benefits of continuous integration and deployment, expectations are ever-increasing. Companies expect every successful dev commit to be available in production in minutes. For large teams, this could imply several hundred or thousand deployments every day. Let’s look at how to continuously modernize the CI/CD pipelines so that they don’t end up in a bottleneck.

• Keep scaling the CI/CD platforms: You must continuously scale the infrastructure needed to provide fast builds and tests for all team members.
• Support for new technologies: As the team starts using new languages, databases, and other tools, the CI/CD platform must keep up.
• Reliable tests: You should have confidence in the automated tests. All tests must be consistent. You must optimize the number of tests to control test execution time.
• Rapid pipeline modification: The team should be able to reconfigure pipelines rapidly to keep up with changing requirements.

Next Steps Toward Continuous Modernization

vFunction, which has developed an AI and data science-powered platform to transform legacy applications into microservices, helps companies on their path towards continuous modernization. There are two related tools:

• vFunction Assessment Hub, which is an assessment tool for decision-makers that analyzes the technical debt of a company’s monolithic applications, accurately identifies the source of that debt and measures its negative impact on innovation
• the vFunction Modernization Hub, which is an AI-driven modernization solution that automatically transforms complex monolithic applications into microservices, restoring engineering velocity, increasing application scalability, and unlocking the value of the cloud.

These tools help organizations manage their modernization journey.

vFunction Assessment Hub measures app complexity based on code modularity and dependency entanglements, measures the risk of changes impacting stability based on the depth and length of the dependency chains, and then aggregates these to assess the overall technical debt level. It then benchmarks debt, risk, and complexity against the organization’s own estate, while identifying aging frameworks that could pose future security and licensing risks. vFunction Assessment Hub integrates seamlessly with the vFunction Modernization Hub which can directly lead to refactoring, re-architecting, and rewriting applications with the full vFunction Modernization Hub.

 vFunction Modernization Hub utilizes both deep domain-driven observability via a passive JVM agent and sophisticated static analysis, vFunction Modernization Hub analyzes architectural flows, classes, usage, memory, and resources to detect and unearth critical business domain functions buried within a monolith.

Whether your application is on-premise or you have already lifted and shifted to the cloud, the world’s most innovative organizations are applying vFunction on their complex “megaliths” (large monoliths) to untangle complex, hidden, and dense dependencies for business-critical applications that often total over 10 million lines of code and consist of 1000’s of classes.The convenience of this approach lies in the fact that all this happens behind a single screen. You don’t need to use several tools to perform the analysis or manage the migration. Contact vFunction to request a demo and learn more.

Many of the world’s businesses are running enterprise applications that were developed a decade ago or more. Companies built the apps using a monolithic application architecture and hosted them in private data centers. With time, these applications have become mission-critical for the business; however, they come with many challenges as they age. Testing legacy code uncovers some of these flaws.

In many cases, companies developed the apps without following commonly accepted best practices like TDD (Test Driven Development), unit tests, or automated testing. The testers usually created a test-plan document that listed all potential test cases. But as the developers added new features and changed old ones, testing use cases may not have kept up with the changes. As a result, tests were no longer in sync with the application functionality.

Thus, testing became a hit-or-miss approach, relying mainly on the domain knowledge of a few veteran employees. And when these employees left the organization, this knowledge departed with them. The product quality suffered. Customers became unhappy, and employees lost morale. This is especially salient these days, in what is being called The Great Resignation.

Poor Code Quality Affects Business: Prevent It By Testing Legacy Code

Poor code quality can lead to critical issues in the product’s functionality. In extreme cases, these issues can cause accidents or other disasters and even lead to deaths. The company’s reputation takes a hit as the quality of its products plummet.

Poorly written code results in new features taking longer to develop. The product does not scale as usage increases, leading to unpredictable performance. Product reliability is a big question mark. Security flaws make the product vulnerable, inviting the unwelcome attention of cyber-attackers.

Current users leave, and new prospects stay away. The company spends more on maintaining technical debt than on innovation in order to boost consumer and employee confidence.

Ultimately, the company’s standing suffers, as do its revenues. Thus, code quality directly affects a company’s reputation and financial performance.

How Do We Define Code Quality?

How do we go about testing legacy code quality, and what characteristics does good code have? There is no straightforward answer, as coding is part art and part science. Therefore, estimating code quality can be a subjective matter. Nevertheless, we can measure software quality in two dimensions: qualitatively and quantitatively.

Qualitative Measurement of Code Quality

We cannot conveniently or accurately assess code quality in this way with tools. Instead, we must measure them by other means, such as code reviews by experts, or indirectly, by observing the product’s performance. Here are some parameters that help us evaluate code quality.

Extensibility

Software applications must keep changing in response to market and competitor requirements. So, developers should be able to add new features and functionality without affecting other parts of the system. Extensibility is a measure of whether the design of the software easily allows this. 

Maintainability

Maintainability refers to the ease of making code changes and the associated risks. It depends on the size and complexity of the code. The Halstead complexity score is one measure of maintainability. (Note that extensibility refers to adding large chunks of code to implement brand new features, whereas maintainability refers to making comparatively minor changes).

Testability

Testability is a function of the number of test cases needed to test the system by covering all code paths. It measures how easy it is to verify all possible use cases. The cyclomatic complexity score is an indicator of how testable your app is.

Portability

Portability shows how easily the application can run on a different platform. You can plan for portability from the start of development. Keep compiling and testing on target operating systems, set compiler warning levels to the highest to flag compatibility issues, follow a coding standard, and perform frequent code reviews.

Reusability

Sometimes developers use the same functionality in many places across the application. Reusability refers to the ease with which developers can share code instead of rewriting it many times. It is easier to reuse assets that are modular and loosely coupled. We estimate reusability by identifying the interdependencies in the system.

Reliability

Reliability is the probability that the system will run without failing for a period of time. It is also called availability. A measure of reliability is Mean Time Between Failures (MTBF).

To summarize, these parameters are difficult to quantify, and we must determine them by observation over a period. If the application performs well on all these measures, it is likely to be high quality.

Related: How to Conduct an Application Assessment for Cloud Migration

Quantitative Measures of Code Quality

In addition, there are several quantitative metrics for measuring code quality.

Defect Metrics

Quality experts use historical data (pertaining to the organization) to predict how good or bad the software is. They use metrics like defects per hundred lines of code and escaped defects per hundred lines of code to quantify their findings.

Cyclomatic Complexity

The Cyclomatic Complexity metric describes the complexity of a method (or function) by a number. In simplistic terms, it is the number of unique execution paths in the code and hence the minimum number of test cases needed to test it. The higher the cyclomatic complexity, the lower the readability, and the higher the maintainability.

Halstead Metrics

The Halstead metrics comprise a set of several measurements. Their basis is the number of operators and operands in the application. The metrics represent the difficulty in understanding the program, the time required to code, the number of bugs testers should expect to find, and others.

Weighted Micro Function Points (WMFP)

The WMFP is a modern-day successor to classical code sizing methods like COCOMO. WMFP tools parse the entire source code to calculate several code complexity metrics. The metrics include code flow complexity, the intricacy of arithmetic calculations, overall code structure, the volume of comments, and much more.

There are many other quantitative measures that the industry uses in varying degrees. They include Depth of Inheritance, Class Coupling, Lines of Source Code, Lines of Executable Code, and other metrics.

The Attributes of Good Code Quality

We have seen that it is problematic to quantify code quality. However, there are some common-sense attributes of good quality:

• The code should be functional. It should do what users expect it to do.
• Every line of code plays a role. There is no bloating and no dead code.
• Frequently run automated tests are available. They provide assurance that the code is working.
• There is a reasonable amount of documentation.
• The code is readable and has sufficient comments. It has well-chosen names for variables, methods, and classes. The design is modular.
• Making changes, and adding new features, is easy.
• The product is not vulnerable to cyber-attacks.
• Its speed is acceptable.

What is Technical Debt?

Technical debt results from a software team prioritizing speedy delivery over perfect code. The team must correct or refactor the imperfect code later.

Technical debt, like the financial version, is not always bad. There are benefits to borrowing money to pay for things you cannot afford. Similarly, there is value to releasing code that is not perfect. You get experience, feedback, and in any case, you repay the debt later on, though at a higher cost. But, because technical debt is not as visible to business leaders, people often ignore it.

There are two types of technical debt. A team consciously takes on intentional debt as a strategic decision. It inadvertently incurs unintentional debt because of monolithic application code.

Again, like financial debt, technical debt is manageable to some extent. Once it grows beyond a point, it affects your business. Then you have no choice but to address it. Technical debt is difficult to measure directly. However, a host of issues inevitably accompany technical debt. You either observe them or find them while testing. Here are some of them:

The Pace of Releasing New Features Slows Down

At some point, teams start spending more time on reducing tech debt (refactoring the code to get it to a state where adding features is not very difficult) than on working on new features. As a result, the product lags behind the competition.

Releases Take Longer

Code suffering from tech debt is code difficult to read and understand. Developers who add new features to this codebase find it difficult and time-consuming. Release cycle times increase.

Poor Quality Releases

Thanks to technical debt, developers take longer than planned to deliver builds to the QA team. Testers have insufficient time to test thoroughly; therefore, they cut corners. The number of defects that escape to production increases.

Regression of Issues

As technical debt increases, the code base becomes unstable. Adding new code almost inevitably breaks some other functionality. Previously resolved defects resurface.

When you face these issues in your organization, you can be sure that you have incurred a significant amount of technical debt and must pay it off immediately.

How to Get Rid of Technical Debt

The best way of paying off technical debt is to stop adding new features and focus only on refactoring and improving the code. List out all your problems and resolve them one by one. Map sets of fixes to releases so that the team continues its cadence of rolling out regular updates.

When these issues get out of hand, focus exclusively on paying off the technical debt. It is time to stop maintaining and start modernizing.

Related: What is Refactoring in Cloud Migration? Judging Legacy Java Applications for Refactoring or Discarding

Differences in Testing Legacy Code vs. New Code: Best Practices for Testing Change Over Time

Often, the only way to pay off tech debt for enterprise applications is to modernize them. But then, how can the team make sure that tech debt does not accumulate again in the modernized apps? It is unlikely because testing a modern application differs from testing a legacy app. Let’s look at some of these differences.

Testing Legacy Code

• Testers have difficulty understanding the complexity of large monolithic applications.
• Fixing defects may have unintended consequences, so testers often expend a lot of effort to verify even minor code changes. The team must constantly test for regression.
• Automated testing is beneficial but has to be done from scratch. Unit tests may not make sense. Instead, integration or end-to-end tests may be more suitable. The team should prioritize the areas to be automated.
• Developers should add automated unit tests when they work on new features.

Testing Modern Applications: Challenges and Advantages

• Modern applications are often developed as cloud-native microservices. Testing them requires special skills.
• The software needs to run on several devices, operating systems, and browsers, so managers should plan for this.
• Setting up a test environment with production-like test data is challenging. Testing must cover performance and scalability.
• Test teams need to be agile. They must complete writing test plans, automating tests, running them, and generating bug reports within a sprint.
• UI/UX matters a lot. Testers must pay a lot of attention to usability and look-and-feel.
• Developers follow Test Driven Development (TDD). Also, Continuous Integration/Continuous Delivery pipelines support running automated test cases. Correspondingly, this improves and maintains quality and reduces the burden on test teams.

Determining Code Quality: the Easy Way

As we have seen, testing legacy code and assessing its quality is a complex undertaking. We have described some parameters and techniques for qualitatively and quantitatively appraising the quality of the code.

We must either use tools to measure these parameters or make manual judgments, say, by doing code reviews. But each tool only throws light on one parameter. So, we need to use several tools to get a complete picture of the quality of the legacy app.

So, to evaluate the quality of legacy code and decide if it is worth modernizing requires us to use several tools. And after we have partially or fully modernized the application, we want to calculate the ROI by measuring the quality of the modernized code. Again, this requires multiple tools and is an expensive and lengthy process.

vFunction offers an alternative approach: using a custom-built platform that provides tools to drive modernization assessment projects from a single pane of glass. vFunction can analyze architectural flows, classes, usage, memory, dead code, class linkages, and resources even in megaliths. They use this analysis to recommend whether modernization makes sense. Contact vFunction today to see how they can help you assess the feasibility of modernizing your legacy applications.

Numerous enterprise applications still run on outdated Java technologies, especially the core functionalities. In some cases, businesses are reluctant to move these applications to the cloud because that could make them obsolete. However, these older systems have multiple weak points and security vulnerabilities as cyber security criminals have become more sophisticated.

Let’s look at legacy Java security and the associated risks.

Understanding Legacy Java Security: What Are Legacy Java Systems?

Legacy systems refer to old mainframe terminal applications used in the 80s and 90s, but today many engineers also think of legacy systems as first-generation, web-based business apps developed in the late 1990s.

Legacy systems’ architectures are different, and they face many risks. As such, the modernization of these legacy systems is essential. Types of legacy systems include:

·   Terminal/ mainframe systems

·   Workstation systems (client/server)

·   Browser/ internet systems

Assessing Legacy System Security Concerns

Hacks and breaches can affect any organization, and black hats’ exploitation of these vulnerabilities has become very common. The causes of cyber-attacks and breaches vary widely, and organizations that rely on legacy applications are the most vulnerable to such attacks. 

According to a report by Security Boulevard, there was an increase in severe and critical vulnerabilities to organizations in 2021. Many of these risks involved Java, including personalized vulnerabilities (which predominantly affect all of the apps analyzed in the survey) and weaknesses that resulted in executable file layer attacks.

Throughout May and June of 2021, Java applications were disrupted more by severe vulnerabilities than .NET applications.

Main Risks of Legacy Java Security

Many data centers still have obsolete Java code with well-documented security flaws, leaving businesses vulnerable to attack.

Zero-day attacks are flaws unearthed in software but not yet patched by the supplier. These weaknesses enable viruses to infect your PC without detection by your browser or anti-virus software, and they do not often come from a dangerous website.

A developer can even find an infected script on well-known and genuine websites. This isn’t a brand-new issue for Java.

Related: Application Modernization and Optimization: What Does It Mean?

Some of the risks of legacy Java security include:

Old Security Practices Doesn’t Evolve at the Same Pace as the Threat Landscape

When developing legacy systems, applications had the top cybersecurity practices current at that time. However, the dynamic threat landscape keeps changing, leaving most legacy systems behind.

Some legacy systems become incompatible with modern security features such as role-based access or single-sign-on and multi-factor authentication. They may also have inadequate encryption methods or audit trails. These reasons prevent legacy systems from accommodating the current security best practices.

Additionally, legacy Java security vulnerabilities in software get significant publicity in industry journals and security blogs. Although it’s essential for security professionals to get these updates, hackers receive the same information. This gives cybercriminals adequate tools and knowledge to exploit the documented vulnerabilities in legacy systems.

Outdated Software, Hardware, or Databases Leads to Legacy Dependencies

Besides legacy applications lacking adequate security features, these apps rely on legacy dependencies to function. Some still rely on dated legacy software, mainframe hardware, operating systems, or database structures. These dependencies lead to more security vulnerabilities.

Most organizations that use legacy databases develop multiple mission-critical, proprietary business systems. Over time, the systems become interconnected since they rely on the same legacy databases.

In-house development teams are not security experts, so it’s easy to overlook the best practices for usability and security. The result is spaghetti code that is challenging, if not impossible, to secure and untangle. Due to this complexity, most organizations delay modernizing the code until a security incident makes it mandatory.

Legacy dependencies can also affect the business process by slowing down or preventing successful application modernization initiatives for the cloud.

Legacy Systems Lack True Security Visibility

Spaghetti code in legacy systems often leaves dead code and outdated frameworks in the production environment. Small apps with dated open source code may fail to appear in IT inventories since they don’t contribute to the main business system functionality, and perhaps only a few people still use them.

These apps and tools don’t undergo active development, so they can create security vulnerabilities if not modernized. According to Jaxenter, a Java development magazine, IT security personnel should be very cautious with unpatched software applications.

All applications, regardless of their size, are potentially accessible to cybercriminals. Eliminating any vulnerabilities in the infrastructure reduces this threat. Modern platforms can leverage full-stack security solutions. Businesses should therefore use such platforms offering additional security SLAs to ensure better visibility into security.

Internal Applications Can Have External Exposure Over Time

Despite having reasonable security measures, businesses that use legacy systems develop more vulnerabilities over time. They arise from the software and hardware abandoned after corporate restructuring, mergers, or acquisitions.

Since these assets don’t get decommissioned, although no one is using them, the legacy software or hardware remains in the background. They get exposed to the external world during IT changes and become unsecured access points to cybercriminals looking for such intrusions.

A great example is when FedEx incorporated a company named Bongo. The legacy storage server remained unnoticed when integrating Bongo’s IT assets with the FedEx environment. This left an unsecured Amazon S3 server online within the FedEx network.

Difficulty in Implementing Additional Security Layers

The design in most modern security packages is incompatible with legacy operating systems and mainframe environments. Most legacy applications also often lack instantaneous security monitoring, making it difficult to identify and address security intrusions.

Legacy systems also have inadequate information to offer the true visibility that security experts need. The log functionality and audit trails can be missing or in a challenging format to access and analyze.

In such a case, cybercriminals can exploit legacy applications without creating logs or triggering alerts. They can access the internal network and other systems undetected, and the IT team cannot identify the original access point.

Unlike legacy systems, deploying applications with a hybrid tech stack or a modern cloud allows quick and manageable security solutions. It’s possible to integrate plug-and-play security and network monitoring solutions compatible with your platform.

Related: Why Legacy Application Modernization Is the New Buzzword

How Can Modernizing (Or Refactoring) Mitigate These Risks?

The solution to most legacy security vulnerabilities is modernizing the tech stack. Continuous modernization eliminates possible future technical debt from accumulating and presenting new security attack vectors to bad actors.

Security Threats to Software

Software security is progressively becoming a significant challenge, as software has come to play an essential role in the actual world, and our lifestyles are heavily dependent on it. A device that can connect to open communication systems like the Internet has a piece of code operating on it, swapping data (or objects) or supplying services and applications over the network systems.

Furthermore, various types of mobile source code (for example, a Java applet or JavaScript) are retrieved over the network systems and run on individual computers worldwide. Most software products are vulnerable to vicious enemies in this perspective.

Unprotected software programs, including defenseless code, should be eliminated as much as possible to minimize the risks and exposure posed by threats that try to exploit known vulnerabilities.

In general, two approaches are available for this. One is to build stable software without any vulnerable code–simple, right? If methodologies that sustain the entire software development process are readily accessible, it becomes less possible to unknowingly build software that is vulnerable to threat.

However, designing and implementing secure software in advance is difficult, and replacing all current unprotected programs with freshly created ones takes longer.

Another strategy is to partly modify the style or code of existing software to make it safer. Likely, this will not eliminate threats and risks altogether, but it can reduce the possibility of hazards (such as data manipulation or data breaches) as soon as possible.

Refactoring To Mitigate Risks

All code degrades over time. You will need to refactor it at some stage. Refactoring refers to enhancing code that has deteriorated due to time, adjustments to frameworks and databases, and generally increasing incongruence with the remaining portion of the software.

While this appears essential, developers frequently overlook refactoring during new software development. This results in an alteration of the internal structure of the existing code without changing its observable behavior.

According to Secure Coding, businesses are often hostile to the refactoring idea and refuse to set aside time for this, partly because coders view refactoring as going backward on an already finished project. But refactoring is about much more than repairing broken code. How will your long-term software investments fare if all code ultimately ages?

Modernizing Java is no easy feat because of the complex dependencies and the need for constant regression testing. Therefore, refactoring to improve the design structure while preserving behavior is a typical move to enhance the quality of software systems.

Refactoring can morph into a continuous modernization best practice for coders as code complexity increases. Complexity results because of the addition of unique features, parameters, and specifications. Almost all of the time, refactoring is a reaction to the increasing necessity to revise existing code to ensure it fits into new trends and best practices.

Refactoring is another way of dealing with the mess built up over the years. No script is flawless, but our misconceptions and faulty logic become apparent the more we write.

Refactoring by inference is one option. Developers use this technique whenever there is a considerable quantity of scripts to refactor. Abstraction aims to remove redundancy. Though duplicate code indicates inefficient code, it can lead to security flaws in which one piece of code gets repaired, but the copy is not.

Secure refactoring boosts the safeguards of current code, regardless of how maintainable it is. It identifies vulnerabilities (unintended code flaws) in software programs and eliminates defenseless code or fragile configurations that hackers could use to intentionally or inadvertently harm assets. 

Judging Legacy Java Applications for Refactoring or Discarding

Every organization with multiple Java applications needs to transition to the cloud as part of its application modernization strategy. Modernizing these applications is vital to ensure business continuity and reduce security threats.

However, there are cases where discarding legacy applications is more viable than modernizing them. These insights will help you determine the best course of action based on your specific circumstances.

Containerization

Java containerization or java microservices is the process of encapsulating software and all of its dependencies in a solitary, portable bundle. Though not a new principle, containerization is gaining traction thanks to technologies from Docker and the Open Container Initiative that are well suited for cloud deployment.

Beyond operating legacy Java in the context of a modern Java running time, Java containerization offers additional protection. Security protocols execution, for instance, can happen within the (contemporary) Java platform.

Since the controls exist within the JVM, they can be very detailed and application-specific. They can prevent illegal access to particular file system aspects or hinder Java functionalities that aren’t in use by the app but frequently encounter malware infections.

The Ultimate Solution to Legacy Java Security Concerns

Due to Java’s widespread use, comprehensive risk management of Java-related tooling and innovations is critical for ensuring strict protection, whether running a complete CI/CD workflow or just a few internal enterprise internet applications. vFunction offers software solutions that work for you. The platform allows you to learn, assess, refactor, scale, and optimize your Java Applications. You can leverage modernization assessments, monolith transformations, and deep dependency discovery. Schedule a demo today to discover modernization and its various benefits to your organization.