An article by Uwe Graf, Head of Consulting at EasiRun.
The discussion around artificial intelligence and COBOL modernization has gained significant momentum. New AI-powered tools promise to analyze even large-scale legacy applications, identify business logic, and translate programs into modern programming languages.
For organizations struggling with a shortage of skilled COBOL developers, rising operating costs, or applications that have become increasingly difficult to maintain, this sounds highly attractive. AI can indeed accelerate many aspects of the modernization process. The challenge arises when technical assistance is mistaken for the promise of largely automated application modernization.
There is a fundamental difference between translating code and delivering a truly modernized enterprise application.
Understanding Source Code Is Not the Same as Understanding the Entire System
Source code captures an important part of an application’s business logic. However, it does not automatically contain all the knowledge required to operate that application safely and reliably.
Long-established enterprise systems typically consist of far more than COBOL programs alone. They include copybooks, JCL, batch processes, databases, files, interfaces, transaction processing systems, and operational procedures. In addition, they often rely on business-specific exceptions and historical design decisions that may never have been fully documented.
An AI model can determine what a program does. What it cannot reliably infer, without additional context, is why a particular behavior exists or whether it is still required.
This is where one of the greatest risks lies. A generated Java application may appear technically correct while still implementing individual business rules differently from the original system.
For this reason, EasiRun does not begin by translating individual programs. Instead, the process starts with a comprehensive analysis of the entire application landscape. Applications, data, dependencies, interfaces, platforms, and operational processes are evaluated together. Only after this analysis can informed decisions be made about which components should be preserved, integrated, transformed, or completely redeveloped.
Compilable Code Does Not Prove Functional Equivalence
AI-generated code is often highly convincing. It is typically well structured, makes use of modern language features, and may compile without a single error.
However, none of this proves that it behaves in exactly the same way as the original COBOL application.
COBOL relies on data structures and language constructs that cannot always be translated directly into Java or other modern programming languages. Examples include REDEFINES, copybooks, packed decimal data types, different character encodings, and implicit type conversions.
The most critical issues are often those that do not cause an immediate program failure. A subtle rounding difference, a misinterpreted numeric value, or an overlooked dependency may remain undetected for a long time.
The most dangerous outcome of an incorrect transformation is therefore not necessarily an application that fails completely. A far greater risk is an application that works correctly in almost every situation but produces incorrect results under specific business conditions.
P3/COBOL supports a controlled transition from COBOL to Java. Organizations can preserve their proven COBOL business logic while generating Java source code from it. COBOL and Java can coexist, allowing suitable components to be modernized incrementally.
This eliminates the need for an all-or-nothing migration. Throughout the transformation process, the existing application remains available as the functional reference against which the new implementation can be validated.
In addition, the development environment provides navigation across programs, copybooks, subprograms, and dependencies, making the broader application context visible—context that can easily be lost when individual source files are analyzed in isolation.
AI-Generated Tests Are Not Enough
Automatically generated test cases can make a significant contribution to modernization projects. They help uncover previously untested areas of an application and accelerate the creation of a comprehensive test suite.
However, they should never be mistaken for independent proof of functional correctness.
If the same AI model generates both the new implementation and the corresponding test cases from the original code, both outputs may be based on the same misunderstanding. If the model incorrectly interprets a data structure or business rule, that incorrect assumption can become embedded in both the application and its tests.
In that case, the tests may pass successfully while merely confirming the consistency of the model’s own mistake.
Robust quality assurance therefore requires additional points of reference. These include verified outputs from the existing production system, representative test data, documented edge cases, and the expertise of experienced business users and developers.
For this reason, EasiRun validates the actual runtime behavior of the application. The existing system and the modernized solution are executed using identical datasets and business scenarios. Any behavioral differences become visible long before they can affect production.
AI can assist with identifying test cases and generating documentation. However, functional correctness should never be derived solely from the same AI analysis that produced the new source code.
New Technologies Do Not Automatically Lead to Better Architecture
Many modernization initiatives implicitly assume that moving to Java, microservices, containers, or cloud platforms will automatically result in lower operating costs, greater flexibility, and improved maintainability.
These benefits are certainly achievable, but they do not arise simply because a different technology stack has been adopted.
A poorly structured application remains difficult to maintain even after a platform migration. Business logic that has never been fully understood does not suddenly become clearer simply because it has been rewritten in Java. Distributed architectures may also introduce new dependencies, increased network communication, and additional operational complexity.
Likewise, automatically generated Java code is not necessarily a modern Java application. In many cases, procedural processing and the original data structures remain largely unchanged. This can be a sensible intermediate step, but it should not be mistaken for the completion of the modernization journey.
The target architecture should therefore be derived from the organization’s actual business, technical, and economic requirements.
EasiRun does not advocate a predefined modernization path in which every application must be rewritten or migrated to the cloud. Depending on the starting point, the most appropriate strategy may be modernization on the existing platform, API-based integration, incremental transformation, or a complete architectural redesign.
Reproducible Transformation Instead of a Black Box
Large-scale modernization projects require every transformation to be transparent, traceable, and repeatable.
To achieve this, EasiRun/SMART applies well-defined transformation rules. Source code is analyzed systematically and transformed at both the syntactic and semantic levels. Given identical inputs, the process consistently produces identical results.
This becomes particularly important when a change affects not just a single program but hundreds or even thousands of application components. A transformation rule can be reviewed, refined, and then applied consistently across the entire application portfolio.
AI can help identify patterns, suggest transformation rules, and detect unusual programming constructs. The transformation itself, however, remains transparent, explainable, and fully verifiable.
AI Is a Tool Within the Modernization Process
EasiRun views artificial intelligence as a powerful complement to established modernization methods and as a key driver of innovative transformation initiatives.
It can explain source code, generate documentation, analyze dependencies, suggest test cases, and assist developers throughout the implementation process. Used appropriately, AI can significantly improve both the speed and transparency of modernization projects.
What it cannot replace is the judgment of people who understand an application’s business context, operational environment, and associated risks.
Experienced developers and business experts possess knowledge that is not fully captured in source code. They understand exceptional business scenarios, operational procedures, and historical design decisions that have accumulated over decades. This knowledge must be incorporated systematically into every modernization initiative.
The real value for organizations therefore does not come from translating as much COBOL into Java as quickly as possible.
It comes from gaining a reliable understanding of the entire application landscape, executing transparent and repeatable transformation steps, preserving proven business logic, and validating that the modernized application delivers the same business outcomes as the original system.
Modernization Is More Than Translation
Artificial intelligence will fundamentally reshape the modernization of legacy applications. It will accelerate analysis, make existing knowledge more accessible, and assist developers across a wide range of tasks.
However, the fundamental requirements of a successful modernization project remain unchanged.
Applications must be understood within their broader operational context. Dependencies must be identified and managed. The target architecture must reflect genuine business and technical requirements. Transformations must remain transparent and reproducible, and the results must be validated for functional equivalence, not merely technical correctness.
This is exactly where EasiRun combines the strengths of modern AI capabilities with decades of legacy modernization expertise, specialized technologies, and a structured, incremental modernization approach.
Because the real question is not how quickly a tool can translate COBOL into Java.
The real question is whether the organization ends up with an application that is functionally correct, technically manageable, and capable of supporting future business requirements for years to come.