Citibank, N.a.

Anantha Veerasami

Robin Jose Kurian

Adar Kiran Danait

Maneet Sharma

Vitthal Ramling Betgar

Balaji Kumar

Richard Eric Lawton

Mayank Vinayaka

Badari Narayana Shanka Prasad

Ryan Peterman

Systems and methods for enhanced software development lifecycle management using a pipeline of machine learning and adaptive models for anomaly detection and action generation. In some aspects, the system may generate a system data stream for an SDLC management platform that stitches together source data from multiple sources, generates a query to a first machine learning model for a user story identifier to generate trend information and anomalies, based on output from a second machine learning model including dynamic thresholds for the anomalies, determining at least one anomaly that satisfies a corresponding dynamic threshold, processing using an adaptive model the at least one anomaly to generate recommended actions to address the at least one anomaly, and generating for inclusion in the graphical user interface the one or more recommended actions.

Machine learning-based anomaly detection and action generation system for software development lifecycle management

Systems and methods for enhanced software development lifecycle management using a pipeline of machine learning and adaptive models for anomaly detection and termination. In some aspects, the system may generate a system data stream for an SDLC management platform that stitches together source data from multiple sources, generates a query to a first machine learning model for a user story identifier to generate trend information and anomalies, based on output from a second machine learning model including dynamic thresholds for the anomalies, determining at least one anomaly that satisfies a corresponding dynamic threshold, based on output from an adaptive model not including recommended actions to address the at least one anomaly, executing a kill switch for the corresponding user story identifier and generating for inclusion in the graphical user interface an indication of the kill switch.

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.

Software development lifecycle traceability tool

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.

Management and presentation of system control data streams

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. In some implementations, the interface includes a plurality of side-by-side smart dials.

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New York, NY, United States of America

Anantha Veerasami