What Is the Role of AI in the COBOL Ecosystem?
Artificial intelligence plays a critical role in revitalizing the COBOL ecosystem by bridging the gap between legacy systems and modern technology demands. AI-driven tools automate labor-intensive tasks such as code analysis, documentation generation, and system refactoring, enabling organizations to manage their COBOL assets more efficiently.
By reducing the manual workload required for understanding, maintaining, and modernizing COBOL applications, AI helps extend the lifespan of mission-critical systems while improving their agility and integration potential with newer technologies.
In addition to modernization, AI enhances the sustainability of COBOL environments by compensating for the declining pool of skilled developers. Intelligent assistants can support code comprehension, suggest optimizations, and even generate COBOL code from business requirements, lowering the expertise threshold needed to maintain and evolve legacy systems.
By embedding AI into COBOL workflows, organizations can mitigate operational risks, accelerate modernization initiatives, and ensure that core business functions built on COBOL remain resilient and adaptable in a rapidly changing IT landscape.
Current Challenges in COBOL-Based Systems
There are several potential issues with using COBOL for modern applications.
1. Aging Codebases and Maintenance Difficulties
Many COBOL systems in use today were written decades ago, often without modern development practices such as modular design, version control, or documentation. As a result, these codebases are difficult to understand, modify, and test. The lack of documentation makes onboarding new developers challenging and increases the risk of introducing bugs during maintenance.
Additionally, the monolithic nature of many legacy COBOL applications complicates efforts to refactor or update them incrementally. In many organizations, business rules are embedded deep within layers of procedural logic, with limited abstraction or reuse. This makes it difficult to extract, verify, or adapt business logic for evolving needs. The accumulation of workarounds and patches further degrades code quality.
2. Diminishing Pool of Skilled COBOL Developers
As COBOL is no longer commonly taught in universities or coding bootcamps, the number of developers familiar with it is steadily declining. Many experienced COBOL programmers are reaching retirement age, and few younger developers are entering the field to replace them. This creates a talent gap that makes staffing COBOL projects increasingly difficult.
The niche skillset also drives up labor costs for COBOL development and maintenance. Organizations often have to rely on a small group of legacy system specialists or contract retirees to keep systems operational. This dependence introduces operational risks and makes long-term planning more complex.
3. Integration Issues with Modern Technologies
COBOL systems were not built with modern architectures or interoperability standards in mind. Integrating them with newer platforms—such as cloud-based services, APIs, or mobile applications—typically requires middleware, data format conversions, or custom interface layers. These additions can introduce latency, increase system complexity, and require ongoing maintenance.
COBOL lacks native support for modern development workflows like continuous integration, automated testing, and containerization. This limits the ability to include COBOL systems in broader DevOps pipelines and hinders efforts to modernize IT infrastructure.
Related content: Read our guide to COBOL programming
AI-Driven Modernization of COBOL Applications
Artificial intelligence is used to modernize COBOL systems for various use cases.
Automated Code Translation
AI tools are increasingly used to convert COBOL code into modern languages such as Java, C#, or Python. These tools use natural language processing and machine learning models trained on large codebases to recognize patterns and semantics in COBOL programs. Unlike traditional line-by-line translators, AI-enhanced systems aim to preserve business logic while restructuring the code for readability and maintainability.
One of the key advantages of AI-driven translation is its ability to abstract and reframe legacy procedural code into modern object-oriented or service-based designs. This can significantly reduce the manual effort required in reengineering applications, making large-scale migrations more feasible. However, these tools often require post-translation validation to ensure functional equivalence, especially for mission-critical applications.
Code Analysis and Refactoring
AI-powered tools can analyze large COBOL codebases to identify patterns, dependencies, and redundant logic. These tools assist in understanding the structure and behavior of legacy systems without requiring full manual code reviews. They can flag dead code, highlight tightly coupled modules, and identify areas with high cyclomatic complexity, which are candidates for refactoring.
Beyond analysis, AI can suggest modularization strategies, extract reusable components, and recommend breaking down monolithic programs into smaller, testable units. This refactoring improves code maintainability and prepares the system for potential migration or integration with modern services.
AI-Powered Code Generation and Assistance
Modern AI development tools can assist COBOL developers by generating code snippets, test cases, and even full program templates based on natural language input or partial code. These tools improve productivity, especially for newer developers who may lack deep COBOL expertise. For example, AI-based assistants can autocomplete syntax, suggest control structures, or offer context-aware recommendations during development.
Some advanced systems also support bi-directional generation, allowing business requirements written in plain English to be translated into COBOL procedures. This reduces reliance on domain experts and helps ensure alignment between business logic and code implementation.
Documentation and Knowledge Extraction
One of the most critical pain points in COBOL systems is the lack of documentation. AI tools can automatically generate technical documentation by parsing source code and extracting function summaries, data flow diagrams, and dependency maps. This helps teams understand what legacy systems do, even when original developers are no longer available.
Natural language models can also generate business-level summaries of code modules, enabling collaboration between technical and non-technical stakeholders. This enables knowledge transfer, improves onboarding for new developers, and supports compliance audits.
Performance Optimization
AI techniques can be used to profile COBOL applications and suggest optimizations based on actual runtime behavior. By analyzing logs and execution traces, AI tools can identify inefficient loops, frequent I/O operations, or unnecessary computations that impact performance. This information guides developers in prioritizing performance tuning efforts.
Machine learning models can also predict the performance impact of code changes or system upgrades, allowing for more informed decision-making. In some cases, AI can recommend alternative algorithms or data structures to improve throughput. These optimizations help extend the life of COBOL systems and ensure they continue to meet performance expectations.
Related content: Read our guide to COBOL modernization
Tips from the expert
5 Best Practices for Integrating AI into COBOL Systems
Here are some best practices to help ensure the successful integration of artificial intelligence into COBOL systems.
1. Comprehensive System Analysis
Before integrating AI into COBOL systems, it’s critical to establish a complete understanding of the system’s architecture and internal logic. This involves identifying key components such as input/output routines, business rule implementations, database interactions, and system dependencies. Use static analysis tools to examine source code structure, and dynamic analysis tools to monitor runtime behavior and performance patterns.
A comprehensive system inventory allows teams to document undocumented features, hidden dependencies, and potential integration points for AI tools. It also highlights risk areas where changes might introduce regressions. This foundational step ensures that AI interventions are targeted, accurate, and aligned with business priorities.
2. Isolate Business Functions
In COBOL systems, business logic is often tightly coupled with presentation layers or infrastructure-specific code, such as terminal handling or batch job scheduling. To apply AI tools effectively, organizations should separate business rules from ancillary code. This can be done by identifying logical boundaries, modularizing code, and extracting reusable routines that encapsulate core business functions.
Isolating business logic allows AI tools to operate on a well-defined problem space, improving the quality of translation, refactoring, or documentation. It also enables easier testing and validation of outputs. For example, if business logic is extracted into standalone modules, these can be verified independently from legacy I/O constraints.
3. Selective Code Transformation
AI tools are not universally accurate across all code types and scenarios. Attempting to modernize an entire COBOL system in one sweep can be risky and cost-prohibitive. Instead, organizations should adopt a selective transformation strategy, choosing parts of the system that offer the greatest return on investment or present the highest maintenance burden.
Candidate modules for transformation could include those with frequent change requests, high complexity, or known performance issues. By applying AI to smaller, well-scoped areas first, teams can evaluate its effectiveness and refine workflows before scaling up. Selective transformation also helps in managing risks, as converted code can be tested and validated in isolation without affecting the rest of the system.
4. Adopt Microservices Architecture
To maximize the benefits of AI-driven refactoring or translation, aim to restructure COBOL code into independent services aligned with a microservices architecture. This involves defining service boundaries around business capabilities and decoupling them from shared state or system-wide dependencies.
AI tools can assist by identifying logical partitions in the code and suggesting modularization strategies. Converting COBOL programs into services makes them more adaptable and easier to integrate with modern development stacks, APIs, and cloud platforms. This architecture also enables parallel development, faster deployment cycles, and incremental system upgrades.
5. Regularly Update AI Models
The performance of AI tools depends heavily on the quality and currency of their training data. As business logic evolves, regulatory requirements change, or new development patterns emerge, AI models must be updated to reflect these shifts. Using outdated models can result in inaccurate code translations, missed optimization opportunities, or incorrect documentation.
Organizations should establish a process for continuously improving AI tools, incorporating feedback from developers, testers, and business users. This could involve retraining models with recent code samples, refining rule sets, or tuning algorithms based on observed outcomes. AI systems should also be tested periodically against benchmark tasks to ensure continued accuracy and relevance
Modernizing COBOL Applications with Swimm
All modernization starts with understanding the applications you have before you can get to the applications you want. Swimm generates the missing context including extracting business rules, understanding complex flows, and helping create specs.
Avoid the trap of modernizing without complete information and shorten timelines, reduce reliance on rare COBOL experts, and ensure complete test cases with Swimm.
