System and method for implementing a platform and language agnostic smart SDK upgrade module

A system for automatically upgrading an application, the system comprising:

• a processor;
• and
• a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to:
• scan for SDK (Software Development Kit) upgrade for an application against a dynamic machine learning (ML) model by implementing ML algorithm for predictions in upgrading the application to a newer version of a programming language specification;
• detect whether training of the dynamic ML model is completed or not;
• execute the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to further cause the processor to perform the following automated processes:
• implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application;
• evaluate the predictive results data to determine whether there is a match for the deprecated reference;
• and
• when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to the newer version of the programming language specification.

A non-transitory computer readable medium configured to store instructions for automatically upgrading an application, wherein, when executed, the instructions cause a processor to perform the following:

• scanning for SDK (Software Development Kit) upgrade for an application against a dynamic machine learning (ML) model by implementing ML algorithm for predictions in upgrading the application to a newer version of a programming language specification;
• detecting whether training of the dynamic ML model is completed or not;
• executing the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the following automated processes:
• implementing the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application;
• evaluating the predictive results data to determine whether there is a match for the deprecated reference;
• and
• when it is determined that there is a match for the deprecated reference, automatically replacing code and upgrading the application to the newer version of the programming language specification.