Abstract: Systems and methods for computer program audit organization may leverage advanced technologies, including large language models (LLMs) and feedback controllers, to automate and streamline the audit process. The system receives audit requests, auto-populates them with relevant details, and breaks them into parts specifying responsive artifacts. The parts are routed to application owners and developers for review and response. A feedback controller interacts with an LLM to modify suggested responses and evidence based on feedback. The system bundles approved files into a package and sends notifications to audit participants.

Abstract: A method for performing reducing noise and false positive event detection in testing is disclosed. The method includes performing scanning of a source file including multiple strings; determining, an entropy value of each string included in the multiple strings; acquiring, from a central repository, a first threshold value; identifying one or more strings having an entropy value higher than the first threshold value; removing the one or more strings having the entropy value higher than the first threshold value from the source file; deriving a second threshold value based on the determined entropy values and the first threshold value; identifying one or more strings having an entropy value lower than the second threshold value; removing the one or more strings having the entropy value lower than the second threshold value from the source file; and executing one or more tests on strings remaining in the source file.

Abstract: Various methods, apparatuses/systems, and media for checking the implementation of the design systems/tool kits in application development are disclosed. A processor accesses a database that stores statistical data corresponding to implementing a new application or feature enhancement into a current application; scans an endpoint and collect corresponding statistical data from the database; generates feedback data based on the collected corresponding statistical data regarding implementing the new application or feature enhancement into a current application; creates a machine learning model configured to be trained to output decision data based on the generated feedback data; trains the machine learning model with the decision data; and generates a score by utilizing the trained machine learning model.

Abstract: Various methods, apparatuses/systems, and media for checking the implementation of the design systems/tool kits in application development are disclosed. A processor accesses a database that stores statistical data corresponding to implementing a new application or feature enhancement into a current application; scans an endpoint and collect corresponding statistical data from the database; generates feedback data based on the collected corresponding statistical data regarding implementing the new application or feature enhancement into a current application; creates a machine learning model configured to be trained to output decision data based on the generated feedback data; trains the machine learning model with the decision data; and generates a score by utilizing the trained machine learning model.

Abstract: A method for performing reducing noise and false positive event detection in testing is disclosed. The method includes performing scanning of a source file including multiple strings; determining, an entropy value of each string included in the multiple strings; acquiring, from a central repository, a first threshold value; identifying one or more strings having an entropy value higher than the first threshold value; removing the one or more strings having the entropy value higher than the first threshold value from the source file; deriving a second threshold value based on the determined entropy values and the first threshold value; identifying one or more strings having an entropy value lower than the second threshold value; removing the one or more strings having the entropy value lower than the second threshold value from the source file; and executing one or more tests on strings remaining in the source file.