From Monolith To Cloud: Automating Your Migration Journey

Introduction - Navigating Cloud Migrations

Migrating applications to the cloud is a complex endeavour with substantial technical and organisational implications. It requires assessing cloud readiness, selecting the right migration strategy, and adapting code where necessary. Automated tools now play a key role in streamlining both analysis and transformation.

This article presents two open-source tools, the CNCF project Konveyor and OpenRewrite by Moderne, that aid in planning and executing Java-based enterprise application migrations. We cover both strategic considerations and technical workflows down to code-level adjustments.

Why It Matters

Cloud platforms promise scalability, resilience, automation, and cost control. Legacy systems, however, are often monolithic and tightly coupled to specific infrastructures. Successful migration demands tailored strategies; neither simple “lift and shift” nor full rewrites are always optimal.

Keys to Success

Effective projects balance cost, functionality, and modernisation. They rely on interdisciplinary teams, clear communication, and tools that deliver transparent analyses and reproducible changes. Konveyor helps evaluate migration needs, while OpenRewrite automates code updates—addressing common issues like classpath conflicts, logging frameworks, configuration, and legacy servlet containers.

Challenges – Making Complexity Visible

Cloud migration is not merely a technical exercise; it is an architectural decision with far-reaching consequences. It affects source code, build processes, infrastructure dependencies, deployment strategies, and security requirements. Common technical obstacles include:

• Tightly coupled components without well-defined interfaces
• Hardcoded configurations that hinder environment changes
• Direct use of infrastructure resources such as local file systems or databases
• Outdated frameworks or unsupported libraries

Another challenge is the lack of a consistent testing strategy. Applications without reproducible builds, automated tests, or Infrastructure-as-Code are difficult to integrate into automated CI/CD pipelines—yet such pipelines are essential for cloud-native operations.

Assessing Cloud Readiness With Konveyor

A central concept we use at QAware in cloud readiness assessments is categorising applications into three groups:

• Cloud-alien: Highly monolithic or technologically outdated applications with minimal decoupling and complex dependencies.
• Cloud-friendly: Exhibit some cloud-native characteristics (e.g., externalized configuration, modular architecture) but do not fully meet all criteria.
• Cloud-native: Designed for the cloud from the ground up. These applications use containers, are stateless, horizontally scalable, and easily automated.

Konveyor uses a similar classification that the analysis generates automatically and transparently. Key checks include:

• Use of outdated or proprietary frameworks
• Reliance on the file system or local state
• Direct database access without abstraction
• Absence of logging and monitoring interfaces

Overview Of The Konveyor Project

The Konveyor project has three key components:

1. A deployable web application called Konveyor Hub for migration planning composed of three modules:

• Application Inventory: Serves as the portfolio management system. It enables you to catalogue applications, associate them with business services, define interdependencies, and add metadata via an extendable tagging model.

• Assessment Module (Pathfinder): A questionnaire-based tool embedded within Tackle that evaluates application suitability for containerization (e.g., Kubernetes). It identifies risks, assesses readiness, and produces reports that help drive an adoption plan.

• Analysis Module: Analyses source code and dependencies using predefined or custom rules. The analysis provides a detailed report outlining all issues that might block a migration to Kubernetes and offers effort estimations.

2. A CLI tool called Kantra that encapsulates the analysis capability into a standalone command-line interface. It analyses source code and dependencies, produces reports that highlight migration blockers and provides actionable insights.

3. A new component to include AI capabilities called Konveyor AI. It is using the analysis results and provides automatic fixes using Generative AI. This component is still evolving, but it is promising.

Konveyor’s analysis uses static code analysis and heuristics, detecting elements such as JDBC statements, web frameworks, hardcoded paths, or sun.* classes. The output is available in HTML or JSON and can integrate directly into CI/CD pipelines.

Example: Analysing A Java Application With The Konveyor CLI Kantra

To analyze a Java application using the Konveyor CLI tool Kantra, you can follow these steps:

1. Install the Kantra CLI by downloading it from the Kantra releases page.

2. Once installed, you can run the following command to analyse your Java application for cloud readiness:

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kantra \
  analyze \
  -i /path/to/your/java/application \
  -o ../out \
  --mode source-only \
  --target cloud-readiness 

3. Review the generated report to identify any migration blockers and actionable insights:

Kantra generates the report in HTML format inside the specified output directory (../out in this example).

With the report you can assess the cloud readiness of your application, identify potential migration blockers, and provide actionable insights for refactoring or modernisation efforts.

Migration Strategies – Three Paths To The Cloud

Based on the analysis results, teams can select a migration strategy that fits their application landscape and business goals. In practice, three core migration strategies have emerged, each with distinct goals, efforts, and risks.

Rehosting (“Lift & Shift”)

The developer of the application moves it to the cloud without modifications. This approach is fast and requires minimal development effort but delivers no structural improvements. It is often used for short-term capacity needs or infrastructure transitions (e.g., data center shutdowns). Technically, this can mean migrating VMs 1:1 to IaaS offerings like AWS EC2 or Azure VM, preserving most existing CI/CD pipelines. Downsides include limited scalability, restricted observability, and modest cost benefits—often leading to “Cloud in Name Only” scenarios.

Refactoring

The developer adapts the application to cloud-native paradigms, typically involving:

• Modularization and decoupling
• Containerization (e.g., Docker)
• Externalizing state (sessions, temp files)
• Using environment variables for configuration
• Leveraging cloud services (e.g., S3, RDS, Kafka)

Refactoring is common for functionally stable but technically outdated applications. It enables debt reduction, observability (Prometheus, Grafana, OpenTelemetry), resilient Kubernetes deployments, and future-proof scaling. Tools like OpenRewrite facilitate this through rule-based, reproducible code changes.

Rebuilding

The developer performs a complete rewrite based on modern cloud architectures. A rewrite is necessary when:

• The current application is unmaintainable
• Business logic has fundamentally changed
• New business models (API economy, platform services) are the focus

Typical technologies include microservices, serverless components (AWS Lambda, Azure Functions), and event-driven designs. Rebuilding is costly but forward-looking, with ROI tied to innovation potential.

Hybrid Approaches

Most real-world migrations blend strategies, for example, refactoring the transactional core while rehosting auxiliary modules, combining quick ROI with long-term modernisation. Successful migration balances business continuity with technical renewal.

OpenRewrite – Automated Code Transformation

While Konveyor focuses on analysis and strategy planning, OpenRewrite takes the next step: automated code modifications based on declarative rules.

At its core, OpenRewrite parses code into a full Abstract Syntax Tree (AST), enabling semantically precise transformations. So-called recipes define the transformation logic as modular, reusable and versionable components.

Common Transformation Recipes

OpenRewrite provides a rich set of community recipes for common migration tasks, such as:

• Replacing outdated libraries
• Migrating Spring Boot 1.x → 2.x → 3.x
• Renaming methods, packages, or classes
• Adding security annotations or upgrading dependencies

Example: Upgrading Spring Boot From 2.x To 3.x

Using Maven, it is easy to integrate OpenRewrite into your build process and apply a specific recipe.

The following example demonstrates how to upgrade a Spring Boot application from version 2.x to 3.4, which includes significant changes such as Jakarta EE namespace migration.

1. Add the following plugin configuration to your pom.xml under <plugins>

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      <plugin>
        <groupId>org.openrewrite.maven</groupId>
        <artifactId>rewrite-maven-plugin</artifactId>
        <version>6.15.0</version> <!-- Update to latest version -->
        <configuration>
          <activeRecipes>
            <recipe>org.openrewrite.java.spring.boot3.UpgradeSpringBoot_3_4</recipe>
          </activeRecipes>
          <activeStyles>
            <style>org.openrewrite.java.SpringFormat</style>
          </activeStyles>
        </configuration>
        <dependencies>
          <dependency>
            <groupId>org.openrewrite.recipe</groupId>
            <artifactId>rewrite-spring</artifactId>
            <version>6.11.1</version> <!-- Update to latest version -->
          </dependency>
        </dependencies>
      </plugin>

2. Run the Maven Rewrite Plugin to execute the upgrade:

Creating Custom Rules

Extending OpenRewrite With Custom Recipes

OpenRewrite lets you go beyond community rules by authoring your own recipes. You can do this declaratively with YAML (compose existing building blocks) or imperatively with Java (full control over AST‑level transformations). A pragmatic middle path is to start from Refaster‑style templates to scaffold simple Java recipes quickly.

Declarative (YAML)

When to use: composition, quick wins, and centrally managed policy roll‑outs.

Example

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# Compose existing recipes to roll out standards
name: com.company.platform.baseline
recipeList:
  - org.openrewrite.java.dependencies.UpgradeDependencyVersion:
      groupId: org.springframework.boot
      artifactId: spring-boot-starter
      newVersion: 3.3.x
  - org.openrewrite.java.RemoveUnusedImports
  - org.openrewrite.java.format.AutoFormat

Pros

• Compose prebuilt recipes (dependency upgrades, search/replace, formatting)
• Fast to author and easy to review in PRs
• Versionable alongside code; simple to parameterize

Cons

• Limited to existing transformations or community recipes

Imperative (Java)

When to use: complex, context‑aware refactorings that require AST access.

Minimal Recipe Skeleton

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@DocumentExample
public class ReplaceSystemOutWithSlf4j extends Recipe {
  @Override public String getDisplayName() { return "Use SLF4J instead of System.out"; }
  @Override public TreeVisitor<?, ExecutionContext> getVisitor() {
    return new JavaIsoVisitor<>() {
      @Override public J.MethodInvocation visitMethodInvocation(J.MethodInvocation m, ExecutionContext ctx) {
        m = super.visitMethodInvocation(m, ctx);
        if (m.getSimpleName().equals("println") &&
            TypeUtils.isOfClassType(m.getSelect().getType(), "java.io.PrintStream")) {
          return m.withName(m.getName().withSimpleName("info"))
                  .withSelect(JavaParser.parseExpression("logger"));
        }
        return m;
      }
    }; }
}

Pros

• Full flexibility for sophisticated transformations
• Can encode company rules, security hardening, and architecture guardrails

Cons

• Higher effort; requires Java/AST know‑how

Refaster Templates (Middle Ground)

Use Refaster‑style before/after patterns to generate Java visitors.

Example

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import com.google.errorprone.refaster.annotation.AfterTemplate;
import com.google.errorprone.refaster.annotation.BeforeTemplate;

@RecipeDescriptor(
    name = "Prefer StringIsEmpty",
    description = "Prefer String#isEmpty() over alternatives that check the string's length."
)
public class StringIsEmpty {
  @BeforeTemplate
  boolean equalsEmptyString(String string) {
    return string.equals("");
  }

  @BeforeTemplate
  boolean lengthEquals0(String string) {
    return string.length() == 0;
  }

  @AfterTemplate
  boolean optimizedMethod(String string) {
    return string.isEmpty();
  }
}

When to use

• Straightforward API migrations (method rename, signature change)
• Wide‑spread call‑site edits where a template expresses the intent

Benefits

• Faster than hand‑crafting visitors from scratch
• Still produces deterministic, reviewable changes

The Potential Of LLMs In Cloud Migrations: Complementary Intelligence

With the rise of powerful Large Language Models (LLMs) like GPT‑5 or Claude 4, the question arises: how can these technologies contribute to cloud migration? Unlike rule‑based tools such as OpenRewrite, LLMs operate probabilistically—leveraging statistical language models trained on large code bases and text corpora.

Potential Applications

Promising use cases for LLMs in migration include:

• Code Analysis: Detecting code smells, outdated patterns, or security vulnerabilities in context, generating improvement suggestions with textual explanations.
• Refactoring Suggestions: Producing semantically meaningful restructurings from prompts or pull requests including corresponding tests.
• Configuration File Migration: Updating YAML, XML, Helm charts, or Dockerfiles for new versions or target environments.
• CI/CD Definition Generation: Creating Jenkinsfiles, GitHub Actions, or GitLab CI configurations from natural language descriptions.
• Documentation: Writing technical explanations, architecture diagram descriptions, or changelogs automatically from code.

Advantages

The strengths of LLMs lie in their:

• Flexibility: Understand semantic relationships across frameworks and programming languages.
• Assistance: Help developers explore unfamiliar technologies or modernise legacy code.
• Rapid Prototyping: Provide quick initial migration recommendations through conversational interfaces.

Limitations

However, LLMs also have significant limitations:

• Non‑Deterministic: Results are not guaranteed to be repeatable, which is an issue for production CI/CD pipelines.
• Black‑Box Nature: Limited transparency into decision‑making and safety guarantees.
• Prompt Dependency: Quality and depth depend heavily on how the user formulates the request. Without the right context, LLMs can produce incorrect or incomplete results.

Outlook

Combined with rule‑based tools like OpenRewrite and analysis frameworks like Konveyor, LLMs could serve as a “creative complement” for:

• Identifying semantic patterns
• Generating custom rewrite recipes
• Automating pull request descriptions, migration documentation, and code comments

A hybrid solution with reproducibility and contextual understanding emerges as a promising approach. Konveyor already explores this direction with its Konveyor AI component, which integrates LLM capabilities into the migration process.

Conclusion: Automation As The Key To Scalable Migration

Migrating to the cloud demands strong architectural understanding, high code quality, and precise planning. It is rarely a linear process—instead, it is an iterative cycle of analysis, decision-making, adaptation, and validation. This is where open-source tools like Konveyor and OpenRewrite demonstrate their full value:

• Konveyor provides structured, reproducible analysis to guide planning and decision-making—from assessing cloud readiness to detecting technologies and evaluating risks.
• OpenRewrite applies rule-based, semantically accurate refactorings, enabling traceable, CI/CD-compatible code transformations with minimal manual effort.

Together, they form a complementary toolset: analysis and execution, strategy and implementation, transparency and automation. For organisations aiming to future-proof their software landscapes, these tools offer a scalable, open, and well-integrated path to the cloud. Free of black-box behaviour, but with clear rules, high reusability, and growing community support.

However, the future lies in the combination: Konveyor and OpenRewrite for structured migration, LLMs for creative enhancements, embedded into DevOps processes and cloud-native principles. Migration thus becomes not a one-time effort, but a continuous modernisation process - scalable, traceable, and automated.