Application Modernization:   AI versus Programming-Language Changer

AI – Artificial Intelligence

Programming-Language Changer Tool

Transformation Methods Relies on statistical plausibility Programming-Language Changer approach ensures that the transformation process is rule-based and deterministic. This means that specific objectives are precisely defined and correctable, ensuring outcomes that are not only accurate but also reliable.  Programming-Language Changer’s deterministic methods provide a clear, controllable, and reproducible pathway for code transformation backed by 30 years of industry experience.
Reproducibility and Validation Predictive models can be erratic and non-transparent, offering outcomes that are influenced by unknown factors which may not be consistently reproducible, leading to risks and potential errors throughout a modernization project.

Being able to validate and reproduce outcomes is crucial in code modernization. Programming-Language Changer’s solution is designed to be controllable with validation in mind, providing a high level of confidence in the transformation results. Because of this methodology and our automation, Programming-Language Changer can ensure little to no code freeze or business disruption.


Model Based Modern AI approaches often rely on contextual relevance to interpret and transform code. While these methods can produce outcomes that appear correct on the surface, they lack the depth of understanding necessary to ensure full functional equivalence. Programming-Language Changer first analyzes the entire code base, builds an Intermediate Object Model representing the programming logic with full functional equivalence of the application before transformation.  By meticulously mapping every element and relationship within the entire codebase, Programming-Language Changer ensures that the transformed code not only retains the original intent but also performs in accordance with modern architecture principles and integrates well with services in the new environment.
Full Functional Equivalence, Reliability and Maintainability Modern AI’s limited contextual understanding might miss critical nuances, potentially compromising the system’s performance and reliability. The goal of most application modernization efforts is to preserve the original intent and function of the application while transforming the codebase, but Programming-Language Changer does so much more than just preserve the functionality. The modernized application also goes through meticulous re-architecting and re-factoring which ensures efficiency, scalability and maintainability in the target environment.

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