Citibank, N.a.

Mayank Vinayaka

Richard Eric Lawton

Robin Jose Kurian

Adar Kiran Danait

Maneet Sharma

Vitthal Ramling Betgar

Balaji Kumar

Anantha Veerasami

Badari Narayana Shanka Prasad

Ryan Peterman

Pranita Santosh Patil

Dinakar Anumolu

Nalini Kavali

Sridatree Mukherjee

Sarthak Rajnikant Patel

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 real-time mapping and visualization generation of system components and inter system communications as well as for the generation of real-time recommendations for architectural recommendations. The systems and methods generate hierarchical workflow mappings of a computational network using event data from software applications lineage logs as well as component and artifact repositories. For example, while event data in software applications lineage logs is conventionally limited to identifying that a given process occurred, the systems and methods use the plurality of events detailed in the software applications lineage logs to create, using an artificial intelligence model, a network mapping of how system components are arranged and interact.

Systems and methods for real-time mapping and visualization generation of system components in software systems

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

Growing community of inventors

Irving, TX, United States of America

Mayank Vinayaka