What is COBOL modernization?

COBOL modernization entails updating COBOL applications, which are typically considered legacy systems but remain vital to enterprise operations. The application modernization process includes adopting the latest software development practices, refactoring COBOL code to modern programming languages or migrating to newer platforms.

Modernizing COBOL systems can help preserve mission-critical business logic and functionality while enhancing maintainability, improving interoperability with today’s tech stacks, increasing scalability and reducing operational costs.

Common business-oriented language or COBOL is one of the most widely used languages for programming mainframes. It’s built specifically for business data processing needs.

COBOL’s first version launched in 1960. Decades later, it endures in an estimated 250 billion lines of code in production use across sectors as varied as financial services, government, logistics, manufacturing and retail.¹ And with mainframe systems continuing as mainstays for enterprises around the world, COBOL remains relevant.

COBOL-based applications are known for their exceptional data handling and file processing capabilities and their reliable and stable performance. However, companies might consider modernized applications for these reasons:

• Compliance and security

• Interoperability

• Lack of expertise

• Maintainability

COBOL’s legacy code might not adhere to current data privacy controls and policies or be regularly deployed with the latest security patches. This makes it vulnerable to cyberattacks and data breaches. As older systems, these systems might not be able to keep up with the sophisticated requirements behind constantly changing regulations.

Integrating outmoded COBOL applications with contemporary infrastructure can be a complicated endeavor. This lack of interoperability can slow innovation and business growth, leading to companies lagging behind their competition.

The number of professionals skilled in COBOL is dwindling. Many COBOL specialists are retiring, while today’s software developers have been trained on modern coding languages. This leaves a gap in the workforce, with fewer programmers available to manage and update COBOL applications.²

COBOL codebases can be verbose and unwieldy, with code documentation that’s insufficient or outdated. Their tightly coupled components make debugging difficult, while adding new features or upgrading existing functionality can be challenging. As such, they often hinder the efficiency and productivity of teams working with and maintaining these systems.

Upon embarking on their COBOL modernization journey, enterprises can take one or more of these routes:

• Encapsulation

• Applying DevOps practices

• Migrating to newer platforms

• Code refactoring

This modernization approach converts monolithic COBOL programs into modular microservices. Business functions are wrapped as microservices then exposed as application programming interfaces (APIs).

Encapsulation allows for swift modernization since it doesn’t require changing the underlying COBOL system. As such, it decreases disruption while integrating with cloud-native architectures and workflows.

Organizations can set up a continuous integration/continuous delivery (CI/CD) pipeline for COBOL programs. The CI/CD pipeline encompasses automated testing and deployment and central repositories managed by version control systems. It also works hand in hand with encapsulation, deploying microservices in containerized environments.

This DevOps workflow streamlines the deployment and delivery of COBOL applications. Businesses can shorten production cycles and speed up the release of new features to customers.

Migration is another strategy for modernizing legacy COBOL systems. It involves shifting on-prem mainframe workloads to contemporary operating systems like Linux or Windows or cloud computing platforms such as Amazon AWS, Google Cloud or Microsoft Azure.

This mainframe modernization process usually requires some changes, such as altering access to data, installing middleware or recompiling COBOL applications to run in their new environments. Providers might also offer modernization services to support rehosting or replatforming workloads and supply tools for real-time performance and security monitoring.

Code refactoring restructures COBOL programs at the source code level. It aims to reduce the technical debt that often accompanies legacy code, which needs to be “repaid” over time through ongoing maintenance. Organizations can choose to optimize existing COBOL code or transform it to a more modern coding language like C#, Java or .NET.

Optimization begins with code analysis and a detailed plan to address inefficiencies, lessen dependencies and remove redundancies. The plan can include standardizing data structures defined within copybooks, developing a more modular design and simplifying complex business logic.

Transforming sprawling COBOL codebases to another programming language can be time-consuming. Instead of a full manual rewrite, development teams can employ automation—including AI-powered code conversion or code translation tools—to help save time and minimize the modernization effort required.

Modernization projects tend to be huge and daunting undertakings. Here are some tips for a smoother road ahead:

• Evaluate and plan

• Start small

• Scale gradually

• Test and document

Assessing COBOL codebases is a key first step to check the state of the code and its complexity, pinpoint areas where modernization can make a meaningful impact and determine the best approach to take. Teams must then create a plan that outlines the modernization process, including scope and timelines.

Starting small helps manage the risks of modernization efforts. For instance, companies can initially tackle simple optimization or refactoring tasks or begin encapsulating or migrating minor business functions. This keeps critical components running while minimizing downtime and limiting the effects of any issues that crop up.

Organizations can then expand to more crucial modules of COBOL systems, applying the lessons learned from previous iterations. This allows them to modernize legacy applications at a steady and sustainable pace and gives them the necessary experience to handle more complex transformations.

Thorough tests must be conducted to validate features and functionality and make sure the modernized system still works as expected. Additionally, any changes must be documented, whether it’s new code or configurations, API definitions or DevOps workflows.

Artificial intelligence can automate and accelerate the COBOL modernization process. Generative AI tools in particular are driven by large language models (LLMs) that have been trained or fine-tuned on programming languages like COBOL, allowing them to analyze mainframe code.

Here are a few modernization tasks AI can help with:

• Code understanding and explanation

• Code generation and refactoring

• Code conversion and optimization

• Test generation and code documentation

Swimm’s application understanding platform, for example, combines static analysis with LLMs to extract business rules and identify dependencies and variable flows for each element in COBOL user screens. Meanwhile, IBM® watsonx Code Assistant® for Z employs AI agents to understand and create natural language explanations of mainframe code and technical documentation.

Some AI-backed apps have chatbot interfaces for generating new code based on natural language prompts. Others blend into IDEs (integrated development environments) to produce real-time inline code suggestions. IBM watsonx Code Assistant for Z goes a step further, automatically refactoring COBOL applications into modular business services.

Multi-agent systems, such as one developed by Persistent Systems, uses multiple AI agents to transform COBOL code into a modern programming language like Java. A coder agent conducts the translation, while a reviewer agent checks the converted code for errors or opportunities for optimization and refinement.

AI-powered tools can automatically generate tests to compare the semantic equivalence of the new code with the original COBOL code. They can also produce inline comments to document what certain code fragments do. 

As with any AI application, teams must still exercise caution, verifying the outputs for accuracy and testing any generated code or suggested fixes or changes.