Mainframe
COBOL to Java, C#
& Cloud
In a world increasingly captivated by automation and artificial intelligence (AI), the allure of using Generative AI for code translation is certainly compelling. However, when we are considering mission-critical applications, the question arises: Is Generative AI truly the best choice? SoftwareMining, a leader in COBOL to Java conversions since 2006, delves into this crucial question. We examine the intricacies of code translation and offer an algorithmic approach grounded in established technologies such as graph theory, state transition networks, and pattern recognition, among others.
"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 a specialized partner like SoftwareMining for legacy system migrations. Our 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.
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.
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