Abstract: The present disclosure relates to a method for automated extraction of rules in a software application code. The method discloses extracting rules embedded in the software application source codes based on a control flow analysis and a data flow analysis. Further, the extracted rules are translated into a target defined format based on mapping of parameters associated with the extracted rules with a pre-stored meta data. The translated rules are analyzed to obtain a validated set of rules.

Abstract: Legacy codes of software applications are required to be modernized and migrated to the latest technology. Migration of legacy codes requires extraction of hidden rules comprised in the application code and translating them to meaningful output which is cumbersome. Thus an intelligence in the entire rule extraction and translation process is required for reducing the complexity and risk. The present disclosure provides automated extraction of rules embedded in software application code using machine learning technique(s) (MLT). In the present disclosure, rules embedded in the software application source codes are extracted based on a control flow and data flow analysis. Further, the extracted rules are translated into a target defined format based on mapping of parameters associated with extracted rules with a pre-stored meta data wherein the mapped parameters are classified into one or more categories using the MLT. The translated rules are analyzed to obtain a validated set of rules.