Bank of America Corporation

Vaasudevan Sundaram

Madhusudhanan Krishnamoorthy

Samrat Bhasin

Srinath M R

Shadab Bubere

Prince Noel Pradeep Santhappa Durai

A code repository stores application code. A code management determines, based at least in part on requested features selected in a graphical user interface, code requirements that include attributes of application code needed to achieve the requested features. The code management system determines, based at least in part on the determined code requirements and the metadata for each entry of application code stored in the code repository, one or more candidate application code entries from the code repository. The code management system presents the candidate application code entries for user selection in the graphical user interface. After receipt of a user selection of a selected application code, the selected application code is provided to a computing device associated with the user.

Reusable code management for improved deployment of application code

A code converter uses machine learning to determine conflicts and redundancies in software code. Generally, the code converter uses machine learning to convert software code into vectors that represent the code. These vectors may then be compared with other vectors to determine similarities between code. The similarities may be used to detect conflicts and/or redundancies created during the development process (e.g., when a developer attempts to change the code).

Software code vectorization converter

A code tracking system monitors usage of application code executed by one or more computing devices. Characteristics of the usage of the application code are determined. Using the characteristics of the usage of the application code, a modification to an entry of application code executed by the one or more computing devices is detected. The detected modification to the entry of application code is communicated to a code management system. The code management system presents the modification to the entry of the application code to a user of the code management system.

Monitoring application code usage for improved implementation of reusable code

Software code converter for resolving redundancy during code development

Software code converter for resolving conflicts during code development

Aspects of the disclosure relate to a machine learning based decentralized business planning system. A computing platform may identify an event likely to impact one or more business operations. Subsequently, the computing platform may receive, for the event, data from one or more sources of data. Then, the computing platform may generate a data structure including a plurality of nodes, where the plurality of nodes corresponds to the received data. Then, the computing platform may authenticate, by utilizing a distributed ledger-based verification system, the plurality of nodes. Subsequently, the computing platform may perform, based on business rules applicable to the one or more business operations, analytics on the plurality of authenticated nodes. Then, the computing platform may generate, based on the analytics, a contingency plan to mitigate the impact to the one or more business operations, and may provide, via an interactive graphical user interface, the contingency plan.

Machine learning based decentralized business planning system

A system includes a production server, a backup server, a telemetry analyzer, a memory, and a hardware processor. The telemetry analyzer takes snapshots of various performance metrics of the production server. The memory stores a log of previous disasters that occurred on the production server. The log includes a snapshot of the production server performance metrics from the time each disaster occurred. The memory also stores recovery scripts for each logged disaster. Each script provides instructions for resolving the linked disaster. The hardware processor uses a machine learning architecture to train an autoencoder. The trained autoencoder receives new snapshots from the telemetry analyzer and generates a reconstruction of the new snapshots. The hardware processor then determines a threshold for distinguishing between server disasters and minor anomalies. This distinction is made by comparing the difference between the reconstruction of the new snapshots and the new snapshots with the threshold.

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Chennai, India

Vaasudevan Sundaram