Abstract: Systems and methods for a software development lifecycle traceability tool. In some aspects, the system may implement a traceability tool that generates a system data stream for an SDLC management platform that stitches together source data from multiple sources, trains a machine learning model to generate progress information and execution risks for a user story on the SDLC management platform, generates a graphical user interface with information for each user story and corresponding progress tracking indicia based on output from the machine learning model, and generates an event-based view of prior actions for each user story and one or more recommended actions to address execution risks.

Abstract: A custom system data stream stitches together aspects of data streams from source systems. In one implementation, an analytics circuit extracts from a first data stream received from a system quality assurance (QA) management system, test case data. The analytics circuit parses metadata, such as a user story identifier, from the test case data. Based on the metadata, the analytics circuit parses, from a second data stream received from a system development lifecycle (SDLC) management computing system, SDLC item data, such as a project identifier. Based on the SDLC item data, the analytics circuit determines at least one impacted computer application. The custom system data stream is bound to a graphical user interface. In some implementations, the interface includes a plurality of side-by-side smart dials.

Abstract: Systems and methods are described for a three-tiered artificial intelligence architecture that synchronizes processing statement deployment across diverse computer networks. The system may receive, at a user interface, a first request for a first recommendation. The first recommendation includes a set of standardized processing statements for implementing on a function. The system may input the first request into a first network component. The first network component includes first, second, and third models. The first model is trained to generate a library of standardized processing statements. The second model is trained to generate a plurality of standardized processing characteristic values for each standardized processing statement in the library. The third model is trained to generate recommendations of sets of standardized processing statements to functions. The system receives an output from the first network component and generates for display the first recommendation based on the output.

Abstract: Systems and methods for a software development lifecycle traceability tool. In some aspects, the system may implement a traceability tool that generates a system data stream for an SDLC management platform that stitches together source data from multiple sources, trains a machine learning model to generate progress information and execution risks for a user story on the SDLC management platform, generates a graphical user interface with information for each user story and corresponding progress tracking indicia based on output from the machine learning model, and generates an event-based view of prior actions for each user story and one or more recommended actions to address execution risks.

Abstract: Systems and methods are described for a three-tiered artificial intelligence architecture that synchronizes processing statement deployment across diverse computer networks. The system may receive, at a user interface, a first request for a first recommendation. The first recommendation includes a set of standardized processing statements for implementing on a function. The system may input the first request into a first network component. The first network component includes first, second, and third models. The first model is trained to generate a library of standardized processing statements. The second model is trained to generate a plurality of standardized processing characteristic values for each standardized processing statement in the library. The third model is trained to generate recommendations of sets of standardized processing statements to functions. The system receives an output from the first network component and generates for display the first recommendation based on the output.

Abstract: Systems and methods are described for a three-tiered artificial intelligence architecture that synchronizes processing statement deployment across diverse computer networks. The system may receive, at a user interface, a first request for a first recommendation. The first recommendation includes a set of standardized processing statements for implementing on a function. The system may input the first request into a first network component. The first network component includes first, second, and third models. The first model is trained to generate a library of standardized processing statements. The second model is trained to generate a plurality of standardized processing characteristic values for each standardized processing statement in the library. The third model is trained to generate recommendations of sets of standardized processing statements to functions. The system receives an output from the first network component and generates for display the first recommendation based on the output.

Abstract: A custom system data stream stitches together aspects of data streams from source systems. In one implementation, an analytics circuit extracts from a first data stream received from a system quality assurance (QA) management system, test case data. The analytics circuit parses metadata, such as a user story identifier, from the test case data. Based on the metadata, the analytics circuit parses, from a second data stream received from a system development lifecycle (SDLC) management computing system, SDLC item data, such as a project identifier. Based on the SDLC item data, the analytics circuit determines at least one impacted computer application. The custom system data stream is bound to a graphical user interface. In some implementations, the interface includes a plurality of side-by-side smart dials.

Abstract: A custom system data stream stitches together aspects of various data streams from source systems. In one implementation, an analytics circuit of a computing system extracts, from a first data stream received from a system quality assurance (QA) management system, test case data. The analytics circuit parses metadata, including a user story identifier, from the test case data. Based on the user story identifier, the analytics circuit parses, from a second data stream received from a system development lifecycle (SDLC) management computing system, SDLC item data that includes a project identifier. Based on the project identifier from the second data stream, the analytics circuit determines at least one computer application impacted by activity associated with the test case data from the first data stream. Items from the source data streams are dynamically identified for inclusion in the custom system data stream. The custom system data stream is bound to a graphical user interface.

Abstract: Systems and methods are described for a three-tiered artificial intelligence architecture that synchronizes processing statement deployment across diverse computer networks. The system may receive, at a user interface, a first request for a first recommendation. The first recommendation includes a set of standardized processing statements for implementing on a function. The system may input the first request into a first network component. The first network component includes first, second, and third models. The first model is trained to generate a library of standardized processing statements. The second model is trained to generate a plurality of standardized processing characteristic values for each standardized processing statement in the library. The third model is trained to generate recommendations of sets of standardized processing statements to functions. The system receives an output from the first network component and generates for display the first recommendation based on the output.