Mainframe
COBOL to Java, C#
& Cloud
In a world increasingly captivated by automation and artificial intelligence (AI), the appeal of using Generative AI for code translation is undeniable. However, when it comes to mission-critical applications, a fundamental question arises:
This article explores the complexities of code translation and highlights SoftwareMining's use of a structured, classical AI-based approach. By leveraging proven techniques such as graph theory, state transition networks, and pattern recognition, SoftwareMining ensures accuracy and reliability in automated code modernization.
As the cost of training large language models (LLMs) continues to decline, as demonstrated by recent advancements such as DeepSeek, the feasibility of retraining AI models for specialized domains becomes increasingly relevant. This raises an important question: how does COBOL-to-Java modernization fit into this shift? Would it be more effective to translate through AI or leverage SoftwareMining's algorithmic approach?
Currently, LLMs function primarily by reflecting and reproducing existing online information - without true reasoning, inference, or deep understanding. With this in mind, consider the following: Would NVIDIA rely on LLMs to design a next-generation chip or diagnose critical hardware issues? Would Amazon completely rethink its AWS infrastructure using LLMs for enhanced performance? Would Microsoft's Office suite be fully replaced by LLM-driven solutions? Would Google integrate LLMs into search? Well - yes, but in a limited and controlled manner.
For most mission-critical systems, the answer remains a resounding no. While AI is an exciting and evolving tool, SoftwareMining's deterministic and structured translation approach ensures accuracy, maintainability, and compliance - qualities essential for enterprise-grade COBOL modernization.
Currently, the primary limitation appears to be the high cost of training such LLMs. However, with advancements like DeepSeek, the prospect of developing AI models that surpass today’s capabilities seems inevitable rather than hypothetical.
This raises an important question about long-term strategy: should organizations continue maintaining COBOL systems, waiting for AI to reach a level where it can autonomously manage code, regardless of language? Perhaps solutions like WatsonX could make this a reality one day. Yet, while we see progress in this direction, such capabilities remain a distant possibility.
For now, most COBOL applications fall under the category of business-critical systems, where accuracy and reliability are non-negotiable. As long as LLMs continue to generate inaccurate or unreliable outputs, they remain unsuitable for handling mission-critical modernization efforts.
"Although AI code-writing tools are helping software engineers do their jobs, a report for GitClear, a software firm, found that in the past year or so the quality of such work has declined. Programmers may be using AI to produce a first draft only to discover that it is full of bugs or lacking concision. As a result, they could be spending less time writing code, but more time reviewing and editing it."
This insight underscores the importance of selecting tried and tested solutions. SoftwareMining's expertise ensures not only accurate translation of COBOL to modern languages like Java and C#, but also addresses complex enterprise needs such as security, compliance, and efficient, reliable code transformation. Additionally, our latest release integrates with ChatGPT to enhance the documentation of translated code.
In the realm of business-critical COBOL to Java conversions, the margin for error is virtually non-existent. An illuminating example comes from one of our earliest engagements with a leading financial institution. During a Java translation of a COBOL batch reporting program, the client identified a mere 1-cent discrepancy in a 1 billion dollar calculation. The algorithmic nature of SoftwareMining translation tool allowed the problem to be rectified quickly.
In contrast, resolving such a complex issue within a Generative AI framework, necessitating a re-training cycle, could prove to be both labor-intensive and time-consuming.
Moreover, while it is technically possible to fine-tune Generative AI through feedback loops in each code generation cycle, this option becomes financially prohibitive if applied across multiple business-critical programs.
AI-based methods like IBM's Watsonx Code Assistant offer enticing automation capabilities but come with complex challenges. A key concern is the 'black-box' nature of AI algorithms, which poses a transparency issue problematic for large organizations requiring stringent governance and code predictability.
For instance, using similar COBOL programs as inputs may yield different Java translations that are functionally correct but have disparate structures. While these varied outputs could all pass functional tests, they introduce an additional layer of complexity to system maintenance. This inconsistency not only complicates short-term validation but also undermines the long-term sustainability and manageability of mission-critical operations.
Such inconsistency is already evident in other Generative AI such as OpenAI's ChatGPT: when tasked with writing a paragraph on a given topic, it can generate multiple variations, each diverging in structure and style.
In summary, while the allure of AI's automation is compelling, the path is fraught with complexities that could be potentially more expensive to resolve compared to algorithmic COBOL to Java conversion.
While Generative AI translations may initially appear cost-effective, it's crucial to examine the entire scope of expenses involved. In our extensive experience, the COBOL to Java translation phase typically accounts for only around 10% of the overall project cost. A staggering 90% of the budget is allocated to testing.
Importantly, test script generation cannot be fully automated. Any errors or incorrect assumptions made during the translation stage are likely to be reintroduced in the generation of corresponding test scripts, further escalating costs.
As a result, an independent and thorough testing mechanism is indispensable. Given that testing constitutes 90% of project expenses, Generative AI does not yield significant cost advantages.
While IBM's Watsonx Code Assistant for Z promises the transformation of COBOL business services into Java, it's important to note the focus on 'selective' and 'incremental' transformation. This approach aligns with IBM's business model, which aims to maintain a dependency on mainframe systems - a significant source of their revenue. Although this may serve some short-term needs, it leaves large organizations tethered to aging infrastructures, thus prolonging the risks and costs associated with legacy systems.
In contrast, SoftwareMining's COBOL Translator aims to offer complete modernization, freeing organizations from mainframe dependencies while ensuring autonomy, security, and flexibility. Our focus is on full-scale, mission-critical COBOL conversions that empower large organizations to move confidently into the future, without the constraints of outdated technology.
Microsoft's Copilot serves as a general-purpose code assistant for a variety of programming languages, offering real-time coding suggestions. However, it is not designed for legacy code migration or to ensure enterprise-level security and compliance requirements, crucial areas where specialized solutions like SoftwareMining excel.
DeepSeek's primary breakthrough lies in significantly reducing retraining costs, albeit with higher inference costs. While this could pave the way for training a specialized model for COBOL-to-Java conversion, several challenges remain.
First, such a model would require access to a substantial and high-quality dataset of COBOL-to-Java translations, which may not be readily available. Additionally, even after successful training, it would still face the same limitations as other LLMs: high computational power requirements during inference and difficulties in consistently reproducing identical results.
These factors make it clear that, while DeepSeek presents an intriguing development in AI model training, its application in mission-critical code modernization remains uncertain.
Our specialized methodology incorporates compiler technologies like Static Single Assignment (SSA) and focuses on formal control-flow analysis for tasks such as the removal of 'GO TO' statements. This algorithmic approach, refined over years of industry experience, stands as a trusted, transparent, and cost-effective alternative to Generative AI solutions. It's not about merely translating code; it's about safeguarding the organization's future through precise and reliable conversions.
