There is arguably no programming language more important than COBOL. Then again, there is arguably no programming language more ripe for change than COBOL. Common Business-Oriented Language or COBOL was developed in 1959 and runs mainframes, the heart of legacy infrastructure, that majority of large corporations rely on.

Being decades old, COBOL applications need to be regularly maintained and documented. And someone has to update this – hundreds of billions of lines of COBOL code that are in use, or new features being added. This is challenging as the process is manual, time consuming and expensive. Another challenge is the workforce experienced in this language is dwindling as they approach retirement age. Generative AI now helps tackle these issues. As discussed in a recently launched report Tech Navigator: The AI-First Organization, these algorithms help redefine the process of code documentation, maintenance, and evolution.

The trade-offs of COBOL

The profound impact that COBOL has on our lives cannot be understated. During the pandemic, New Jersey state governor Phil Murphy pleaded for COBOL programmers to help with the state’s aging mainframe system that saw an unprecedented rise in unemployment claims. Billions of lines of COBOL are in use worldwide and are increasing every year. Governments and the private sector are still heavy users. Many Fortune 500 companies still rely on COBOL for critical work. A significant number of banking systems and a majority of ATMs are still powered by COBOL.

COBOL has stood resilient for decades. Yet, many organizations are struggling to cope with its maintenance. The scarcity of updated documentation arises from multiple factors: retiring developers, incomplete or outdated records, and the sheer scale of these legacy systems. These coupled with decades of system modifications, personnel turnover, and organizational changes have led to a scenario where understanding the intricacies of COBOL codebases has become a formidable challenge. Developers tasked with understanding, updating, or enhancing COBOL programs struggle due to the lack of clear insights into the code's structure, variable meanings, and program flow. Vital information about variables, functions, and program flows is often hidden within the cryptic lines of code, making the process of software maintenance difficult.

Gen AI – a much needed solution

To address the challenges that plagues COBOL systems, corporations are turning to innovations such as generative artificial intelligence algorithms. These algorithms, built on deep learning models, help transform the process of code documentation. AI systems can analyze COBOL codebases and generate comprehensive, accurate, and readable documentation by leveraging advanced pattern recognition and natural language understanding. Variables are accurately described, functions are defined with clarity, and program flows are meticulously outlined.

The integration of generative AI algorithms into COBOL modernization processes offers a myriad of advantages. First, it relieves developers of the arduous task of creating documentation from scratch. Time and resources that were previously allocated to deciphering legacy code and drafting documentation, a significant portion of them can now be redirected towards more value-added tasks such as innovation and problem-solving. Here AI augments rather than replace human capabilities. By using AI assistants, based on generative AI, it helps developers streamline their work.

Moreover, this AI-powered approach augments software maintainability. The comprehensive documentation generated by these algorithms becomes a record of the system's inner workings. As a result, software updates, bug fixes, and enhancements can be executed more seamlessly, with developers having a clear understanding of the code's behavior and dependencies.

As experienced developers retire or move on, the knowledge gap can be challenging to bridge. AI-generated documentation acts as a bridge, facilitating smoother knowledge transfer between generations of developers.

Comprehensive documentation facilitates better decision-making when it comes to code refactoring, enhancement, or migration strategies. Organizations can confidently pursue modernization initiatives armed with a clear understanding of the existing codebase.

A paradigm shift in code documentation

The integration of generative AI techniques into COBOL modernization processes signifies a significant shift in software engineering. By augmenting the process of code documentation, it unlocks the potential for improved system maintainability, reduced costs, and accelerated innovation.

The importance of legacy systems cannot be underestimated. They serve as the bedrock upon which modern businesses operate. Generative AI algorithms pave the way for these systems to not only persist but thrive, by bridging the gap between the old and innovation. The introduction of AI-driven documentation offers a pragmatic solution that aligns with the pace of technological change. By bridging the documentation gap and enhancing the understanding of COBOL systems, this approach empowers businesses to make strategic decisions regarding their software infrastructure.

• Photo by Mohamed Nohassi on Unsplash