Mainframe
COBOL to Java, C#
& Cloud
Automation and artificial intelligence (AI) are transforming the way software is built. It is tempting to imagine Generative AI handling COBOL-to-Java modernization automatically. But for mission-critical systems, the real question is whether this approach delivers the accuracy and consistency that enterprises demand.
This paper examines the difference between generative and algorithmic translation. SoftwareMining applies a structured, deterministic method that uses graph theory, state transitions, and pattern recognition to ensure reliable and repeatable results in automated modernization.
The falling cost of training large language models (LLMs) makes domain-specific AI appear attractive. Yet COBOL-to-Java migration requires more than language prediction; it needs full logical equivalence. This is where SoftwareMining's algorithmic approach continues to outperform generic Generative AI solutions.
Large language models (LLMs) work by reproducing patterns found in existing data. They do not perform true reasoning, inference, or deep system analysis. This raises an important question: how suitable are they for engineering-level modernization?
Would NVIDIA design its next-generation chips with an LLM? Would Amazon rebuild its AWS infrastructure through generative prompts? Would Microsoft replace its core Office products with AI alone? Even Google, a leader in AI research, uses LLMs only in limited and carefully controlled ways.
For mission-critical systems, the answer is clear: no. AI is a powerful and evolving tool, but it cannot yet guarantee the precision required for enterprise modernization. SoftwareMining's deterministic translation approach delivers the accuracy, maintainability, and compliance that large organizations demand for COBOL-to-Java transformation.
The main limitation today is the high cost of training large language models (LLMs). However, with advancements such as DeepSeek, the emergence of AI models that exceed current capabilities seems inevitable rather than hypothetical.
This raises a key strategic question: should organizations keep maintaining COBOL systems and wait for AI to become capable of managing code autonomously, regardless of language? Solutions like WatsonX may eventually make this possible, but for now, such capabilities remain a distant prospect.
Most COBOL applications are business-critical systems where accuracy and reliability cannot be compromised. As long as LLMs continue to produce inconsistent or unreliable results, they are not suitable for mission-critical modernization projects.
A recent article in The Economist discussed how businesses are actually using generative AI:
"Although AI code-writing tools are helping software engineers do their jobs, a report from GitClear, a software firm, found that the quality of such work has declined. Programmers may use AI to create a first draft only to find that it is full of bugs or lacks concision. As a result, they may spend less time writing code but more time reviewing and editing it."
This insight highlights the value of using proven, deterministic tools instead of experimental AI systems. SoftwareMining's technology ensures accurate and maintainable COBOL-to-Java and C# translation while meeting enterprise requirements for security, compliance, and performance. Our latest release also integrates with ChatGPT to enhance code documentation, combining automation with clarity for long-term maintainability.
In business-critical COBOL-to-Java conversions, the margin for error is almost zero. One early example involved a major financial institution where a translated COBOL batch reporting program showed a one-cent difference in a one-billion-dollar calculation. Because SoftwareMining uses an algorithmic and fully deterministic translation process, the issue was quickly identified and corrected.
By contrast, resolving a similar problem using Generative AI could require retraining the model, a process that is both time-consuming and resource-intensive. Although feedback-based fine-tuning is possible with Generative AI, applying it across multiple enterprise systems would be financially impractical for most organizations.
AI-based solutions such as IBM's Watsonx Code Assistant offer impressive automation but also introduce serious challenges. The main concern is their "black-box" nature, which limits transparency and makes them difficult to govern in enterprise environments that demand predictability and auditability.
Even when given similar COBOL inputs, Generative AI can produce different Java translations that are functionally correct but structurally inconsistent. While each version may pass functional tests, the variations increase maintenance effort and make long-term system management more complex. This lack of consistency adds cost and risk to modernization projects.
The same behavior can be seen in tools like ChatGPT, which generate multiple valid yet stylistically different outputs for the same prompt. For code translation, such variability is unacceptable in business-critical systems.
In summary, while Generative AI offers attractive automation, its unpredictability and high cost of correction make it a risky choice compared to deterministic, algorithmic COBOL-to-Java conversion.
At first glance, Generative AI may seem like a cost-effective solution for COBOL-to-Java translation. However, experience shows that translation itself represents only about 10% of a modernization project's total cost. The remaining 90% is spent on testing and validation.
Testing cannot be fully automated. Any errors or incorrect assumptions made during translation are likely to be repeated in the test scripts, which increases both cost and effort. This makes independent and detailed testing essential to ensure system accuracy and reliability.
Given that testing consumes the majority of project resources, Generative AI provides little to no cost advantage compared to structured, algorithmic translation methods.
The rapid rise of Generative AI has introduced new possibilities for code generation and automation. Models such as ChatGPT, Code Llama, Copilot, and IBM's Watsonx Code Assistant can create or refactor code in real time, often producing impressive short-term results. However, these systems operate on probabilistic inference, meaning the same input can yield different outputs—an unacceptable risk for mission-critical enterprise systems.
SoftwareMining takes a fundamentally different approach. Our deterministic COBOL-to-Java translation framework relies on algorithmic analysis, control-flow mapping, and proven static rules. This ensures consistent, verifiable, and auditable results that maintain full functional equivalence with the original COBOL programs—without the unpredictability or governance challenges of generative AI.
IBM's Watsonx Code Assistant for Z offers automated COBOL-to-Java transformation as part of its modernization suite. However, its design focuses on selective and incremental modernization, integrating new Java components with existing mainframe services rather than replacing them entirely. This hybrid strategy aligns with IBM's broader business model, which emphasizes extending the life of mainframe environments that continue to generate significant enterprise revenue.
While this approach can help organizations modernize specific business functions, it often leaves core applications tied to mainframe infrastructure. Such dependency maintains operational constraints and ongoing platform costs, limiting the flexibility and independence that many enterprises seek from modernization.
In contrast, SoftwareMining's COBOL Translator enables complete modernization. Our solution removes mainframe dependencies and produces clean, object-oriented Java applications ready for cloud or on-premise deployment. By ensuring autonomy, security, and scalability, SoftwareMining empowers organizations to move confidently beyond legacy systems and fully embrace open, maintainable technologies.
OpenAI's ChatGPT, powered by GPT-4, excels at generating natural language and assisting with general programming tasks. However, it is not built for the precision required in enterprise COBOL-to-Java translation. Each prompt can produce different code structures and logic flows, making consistent verification and compliance nearly impossible at scale.
Meta's Code Llama is an open-source large language model trained for code generation and completion across popular programming languages. While flexible and fast for general development, it is not designed to handle the structured logic, data dependencies, and compliance demands of mainframe modernization. As with other generative models, Code Llama can produce multiple valid outputs for the same COBOL input, introducing inconsistency and verification challenges.
SoftwareMining delivers predictable, repeatable COBOL-to-Java translation through deterministic algorithms rather than probabilistic text generation. This ensures functional accuracy, maintainability, and full traceability-qualities essential for mission-critical modernization projects that open-source LLMs cannot guarantee.
Microsoft's Copilot is a general-purpose AI assistant designed to help developers write and edit code across multiple languages in real time. While powerful for productivity, it is not built for large-scale legacy code migration or enterprise modernization. Copilot does not provide deterministic outputs, traceable logic conversion, or guaranteed compliance-capabilities that are essential in regulated, mission-critical environments.
SoftwareMining takes a specialized approach. Our deterministic COBOL-to-Java translation framework ensures accuracy, security, and maintainability across entire enterprise portfolios. By focusing exclusively on mainframe modernization, SoftwareMining delivers consistent, auditable, and production-ready Java applications-outcomes that general-purpose AI tools cannot replicate.
Google's Gemini models integrate reasoning and multimodal capabilities for a broad range of creative and analytical tasks. Although powerful for general development, Gemini is not specialized for legacy code migration and cannot guarantee deterministic behavior or data integrity in converted applications.
SoftwareMining provides a proven, rule-driven translation framework specifically designed for mainframe modernization. Our process guarantees functional equivalence, minimizes risk, and supports enterprise-grade governance-offering reliability and consistency that general-purpose AI models cannot match.
SoftwareMining's specialized methodology applies advanced compiler techniques such as Static Single Assignment (SSA) and formal control-flow analysis to optimize translation accuracy. This includes structured refactoring tasks like the removal of GO TO statements and redundant logic. Our algorithmic approach, refined through years of enterprise experience, provides a transparent, verifiable, and cost-effective alternative to Generative AI. It is not just about converting COBOL to Java; it is about protecting an organization's future through precise, maintainable, and reliable modernization.
