Articles

Legacy code refactoring is a critical challenge for CTOs managing aging systems like COBOL. While manual refactoring offers high accuracy, it's costly and time-consuming, and generative AI lacks the reliability to handle complex legacy systems. Transpiler-based tools strike the right balance, achieving 85-95%+ accuracy while reducing cost and time. This article explores the six key pillars of evaluation—Cost, Time, Accuracy, Equivalence, Risk, and Maintainability—and how Recodex’s proven framework helps CTOs modernize with confidence. Discover why legacy application modernization demands a tailored approach and how to choose the right method for your organization.

Legacy application modernization is crucial for industries relying on COBOL, but using Neural Machine Translation (NMT) models for legacy code refactoring poses challenges. NMT models struggle due to limited COBOL training datasets, contextual blindness caused by token limits, and risks of hallucination. While NMT models excel at documentation and modular modernization, tertiary language transpilers remain the superior solution for accurate, large-scale COBOL migration. RecodeX leverages decades of expertise to combine NMT innovation with proven techniques, paving the way for seamless modernization.

Legacy code refactoring, especially for COBOL systems, is a daunting challenge due to differences in syntax, native capabilities, and implementation styles. While static transpilers fall short in addressing these complexities, tertiary language-based transpilers offer a transformative solution. By leveraging Abstract Syntax Trees and context-aware processing, they bridge gaps, preserve intent, and produce modular, maintainable code. Discover how tertiary language transpilers simplify legacy application modernization and ensure seamless COBOL

Refactoring legacy code like COBOL to modern languages is a daunting challenge due to differences in syntax, native capabilities, and implementation styles. Issues such as data type mismatches, global variable handling, and procedural constructs make manual refactoring costly and time-intensive. While static transpilers struggle with implementation differences, modern transpilers using tertiary languages bridge these gaps, achieving 85-95%+ accuracy. Discover how tertiary language-based transpilers are transforming legacy application modernization with faster, more reliable results.