Abstract: Embodiments of the present invention provide a system for identifying, splicing, and formatting code via machine learning for application analysis. In particular, the system may generate a repository that links multiple historical codes with associated analysis parameters. When a request for a new set of analysis codes is requested for analyzing a first application, the system can determine a set of analysis parameters associated with that first application and determine the set of analysis codes for analyzing the first application through machine learning analysis of the repository. The system may then generate the set of analysis codes by splicing together the determined set of analysis codes and formatting the codes into a format that is compatible with the first application. The system may then automatically execute the code on the first application within a testing environment and display the results on a computing device of an associated user.

Abstract: Embodiments of the present invention provide a system for identifying, splicing, and formatting code via machine learning for application analysis. In particular, the system may generate a repository that links multiple historical codes with associated analysis parameters. When a request for a new set of analysis codes is requested for analyzing a first application, the system can determine a set of analysis parameters associated with that first application and determine the set of analysis codes for analyzing the first application through machine learning analysis of the repository. The system may then generate the set of analysis codes by splicing together the determined set of analysis codes and formatting the codes into a format that is compatible with the first application. The system may then automatically execute the code on the first application within a testing environment and display the results on a computing device of an associated user.