Abstract: An end-to-end approach for (i) identifying potential vulnerabilities in software applications, including security vulnerabilities, (ii) verifying/confirming the potential vulnerabilities as actual vulnerabilities and (iii) in response, identifying the necessary remedial actions necessary to eliminate or at least mitigate the vulnerabilities. An intelligent agent is implemented that is configured to detect a change to the application's code or computing environment and, as a result of detection of changes to the code or computing environment, identify potential vulnerabilities, verify/confirm the potential vulnerabilities as actual vulnerabilities through determination/creation of vulnerability test cases and automatically identify the necessary remedial actions necessary to eliminate or mitigate the vulnerabilities.

Abstract: An end-to-end approach for (i) identifying potential vulnerabilities in software applications, including security vulnerabilities, (ii) verifying/confirming the potential vulnerabilities as actual vulnerabilities and (iii) in response, identifying the necessary remedial actions necessary to eliminate or at least mitigate the vulnerabilities. An intelligent agent is implemented that is configured to detect a change to the application's code or computing environment and, as a result of detection of changes to the code or computing environment, identify potential vulnerabilities, verify/confirm the potential vulnerabilities as actual vulnerabilities through determination/creation of vulnerability test cases and automatically identify the necessary remedial actions necessary to eliminate or mitigate the vulnerabilities.

Abstract: Aspects of the disclosure relate to strategizing distributed systems using quantum simulation. A quantum simulation platform may train a quantum simulation model (QSM) and an encoding model. The platform may generate a digital twin of a distributed software system (DSS). The platform may generate a directed acyclic graph (DAG) based on source code for updating the DSS. The platform may generate encoded vectors using the DAG and the encoding model. The platform may generate a source code matrix based on generating distribution assessments for the encoded vectors. The platform may generate a configuration matrix based on generating change vectors using the digital twin. The platform may generate execution scenarios and optimization scores using the QSM, based on inputting the matrices. The platform may identify an optimal execution scenario. The platform may cause one or more updates the DSS based on the optimal execution scenario.

Abstract: A computing platform may configure a dependency knowledge graph indicating file dependencies for mainframe applications, and an error knowledge graph indicating errors and corresponding solutions for the mainframe applications. The computing platform may receive mainframe source code. The computing platform may analyze, using the knowledge graphs, the mainframe source code to identify potential errors and corresponding solutions. Based on identifying an error in the mainframe source code, the computing platform may cause the mainframe source code to be updated according to the corresponding solution. The computing platform may analyze, using the dependency knowledge graph and the error knowledge graph, the updated mainframe source code to identify remaining errors.

Abstract: A computing platform may configure a dependency knowledge graph indicating file dependencies for mainframe applications, and an error knowledge graph indicating errors and corresponding solutions for the mainframe applications. The computing platform may receive mainframe source code. The computing platform may analyze, using the knowledge graphs, the mainframe source code to identify potential errors and corresponding solutions. Based on identifying an error in the mainframe source code, the computing platform may cause the mainframe source code to be updated according to the corresponding solution. The computing platform may analyze, using the dependency knowledge graph and the error knowledge graph, the updated mainframe source code to identify remaining errors.

Abstract: A stand-alone embedded internet of things edge artificial intelligence device (“EIEAC”) may be provided. The EIEAC may be configured to operate independent of the computing device and may be configured to monitor and repair failures occurring at the computing device. The EIEAC may receive computer health data from a client agent application running on the computing device. The EIEAC may include a memory configured to store the computer health data and a battery configured to charge the EIEAC. The EIEAC may include a processor configured to process the computer health data to identify one or more failures using tiny machine learning (“tinyML”). When one or more failures are identified by the processor, the tinyML may be configured to execute instructions on the computer health data for repairing the one or more failures. When unsuccessful, the EIEAC may establish an electronic connection with a remote cloud lookup tower for repairing.

Abstract: A stand-alone embedded internet of things edge artificial intelligence device (“EIEAC”) may be provided. The EIEAC may be configured to operate independent of the computing device and may be configured to monitor and repair failures occurring at the computing device. The EIEAC may receive computer health data from a client agent application running on the computing device. The EIEAC may include a memory configured to store the computer health data and a battery configured to charge the EIEAC. The EIEAC may include a processor configured to process the computer health data to identify one or more failures using tiny machine learning (“tinyML”). When one or more failures are identified by the processor, the tinyML may be configured to execute instructions on the computer health data for repairing the one or more failures. When unsuccessful, the EIEAC may establish an electronic connection with a remote cloud lookup tower for repairing.