Citibank, N.a.

Robin Jose Kurian

Maneet Sharma

Adar Kiran Danait

Vitthal Ramling Betgar

Anantha Veerasami

Balaji Kumar

Balaji Kobula Madhavan

Richard Eric Lawton

Mayank Vinayaka

Ryan Peterman

Badari Narayana Shanka Prasad

Shweta Bammi

Joseph Julius Bosco Arockia Dass

William Franklin Cameron

Laura Mcneil

Jamaal D Marshall

MadhuShalini Kuruganthy

Neelima Rath

Systems and methods are described that allow for updating of software applications during testing of the application to detect errors as a result of execution of portions of the software application that prevent downstream portions of the software application from being evaluated. In an example, systems are described that are configured to detect errors during application execution. When an error is detected, the system obtains and executes specific script sets to debug the application. Based on the results of these debug operations, the system generates error reports that indicate issues within the software workflow. This ensures that errors in one part of the software do not hinder the evaluation of subsequent parts, allowing for a more efficient and thorough testing process.

Systems and methods for integrating script development and script validation platforms based on detected dependency branches in script code

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

Systems and methods for tracking and verifying software capabilities may include obtaining, by a computer from a reporting database, a set of project requirements associated with a software under development, the set of project requirements replicated from a project database to the reporting database, obtaining, from the reporting database, a set of testing results generated by a test program applied to code of the software under development, the set of testing results replicated from a testing database to the reporting database, extracting, from the reporting database, each project requirement associated with the software under development and each testing result generated for the code of the software under development, wherein the computer extracts each project requirement and each testing result according to a set of mapping pre-configurations, and generating, by the computer, an email message combining the project requirement and the testing result to transmit to a set of recipients.

Core decision engine for managing software development lifecycles

A network system to analyze a combined output of various input files from data-based applications. The system provides custom profiling of the data from each application based on an application of one or more sets of rules. The system stores the data from any other number of applications in a base level of granularity to allow direct comparison of the data from each application output. Because the data is stored at a same level of granularity, the data may be compared or processed regardless of the application from which the data is received. The system applies rules to compare the data across the applications to identify outliers, trends, or commonalities. The system may also search for and identify data fitting a specific rule across the applications to extract, modify, or label the data. The system provides a visualization of the data based on the rules applied.

Automatic validation of input files

Systems and methods provide for an integrated script development and script validation platform. The integrated script development and script validation platform archives data in a way such that the dependencies between contributions of code strings (e.g., script sets) are detected and recorded. That is, the systems and methods detect dependency branches in the script code of script sets. By doing so, the systems and methods may identify individual performance characteristics for a given script set as well as determine the overall impact on the application itself.

Presented herein are systems and methods for tracking and verifying software capabilities. A method may include obtaining, by a computer from a reporting database, a set of project requirements associated with a software under development, the set of project requirements replicated from a project database to the reporting database, obtaining, from the reporting database, a set of testing results generated by a test program applied to code of the software under development, the set of testing results replicated from a testing database to the reporting database, extracting, from the reporting database, each project requirement associated with the software under development and each testing result generated for the code of the software under development, the computer extracting each project requirement and each testing result according to a set of mapping pre-configurations, and generating an email message combining the project requirement and the testing result to transmit to a set of recipients.

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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Carrollton, TX, United States of America

Robin Jose Kurian