Abstract: Methods, system, and non-transitory processor-readable storage medium for UI iconography validation system are provided herein. An example method includes receiving, by a user interface (UI) iconography validation system, a plurality of icons under test. The UI iconography validation system creates tuples by pairing each icon under test with each valid icon from an icon database and processes the created tuples through a trained Siamese neural network model to generate matching scores. The UI iconography validation system determines a validation status of each icon under test based on the matching scores, where an icon is validated if any of its tuples generates a positive matching score and flags icons that fail to generate any positive matching scores as invalid icons.

Abstract: Methods, system, and non-transitory processor-readable storage medium for email targeting system are provided herein. An example method includes receiving by an email server system, from an email client system, an email campaign intended for a plurality of recipients. The email targeting system categorizes each of the plurality of recipients into recipient behavior classifications based on previous recipient behavior responding to previous email campaigns. The email targeting system generates a personalized templated email communication for each of recipient behavior classifications, to replace the email campaign, where the email targeting system uses the email campaign, a large language model, and the recipient behavior classifications to generate the personalized templated email.

Abstract: Techniques for providing a centralized framework for forecasting IT component failures. The techniques include collecting raw telemetry data specific to different IT component domains, and transforming the telemetry data into structured telemetry data. The techniques include performing feature engineering on the structured telemetry data to obtain features relevant to IT component failures in each IT component domain, and, for each IT component domain, using the features to generate a customized ML model. The techniques include accessing features relevant to IT component failures in each IT component domain, accessing a customized ML model for forecasting IT component failures in the IT component domain, and forecasting IT component failures in the IT component domain using the customized ML model.

Abstract: Methods, system, and non-transitory processor-readable storage medium for UI design kit approval system are provided herein. An example method includes receiving, by a Continuous Integration/Continuous Delivery (CI/CD) pipeline runner system, a configuration interface defining User Interface (UI) design standards validation parameters for source code. The UI design kit approval system scans the source code to validate UI design standards associated with the source code using the configuration interface. The configuration interface is received by the UI design kit approval system via a UI design approval integration interface that integrates with the CI/CD pipeline runner system. The UI design kit approval system generates a UI design standards validation report indicating a compliance assessment of UI design standards associated with the source code.

Abstract: Methods, system, and non-transitory processor-readable storage medium for a text-based vector retrieval system are provided herein. An example method includes generating, by a text-based vector retrieval system, a set of topics Tx for a document set DX, generating for each document DN a set of topics TD, comparing the set of topics TD against the set of topics TX to generate a filtered set of topics TDFinal, comparing the topics TDFinal against final topics of all other documents to generate a set DNMatching, encoding each document from the document set into a vector database with corresponding DNMatching and TDFinal as metadata, grouping vectors into N search clusters, retrieving N topics from a query string to generate TQuery topics, comparing the TQuery topics against topics in each cluster to identify matching clusters having R matching topics, and calculating similarity scores for documents in the matching clusters.

Abstract: Techniques for providing a centralized framework for forecasting IT component failures. The techniques include collecting raw telemetry data specific to different IT component domains, and transforming the telemetry data into structured telemetry data. The techniques include performing feature engineering on the structured telemetry data to obtain features relevant to IT component failures in each IT component domain, and, for each IT component domain, using the features to generate a customized ML model. The techniques include accessing features relevant to IT component failures in each IT component domain, accessing a customized ML model for forecasting IT component failures in the IT component domain, and forecasting IT component failures in the IT component domain using the customized ML model.

Abstract: Methods, system, and non-transitory processor-readable storage medium for a simulation analysis system are provided herein. An example method includes monitoring, by a simulation analysis system, user interactions with User Interface (UI) elements on a user interface to obtain click patterns associated with the UI elements. The simulation analysis system generates simulated user profiles based on the user interactions. The simulation analysis system calculates a User Interface/User Experience (UI/UX) impact using the click patterns associated with the simulated user profiles, and identifies a set of high impact UI elements associated with the UI.

Abstract: Methods, system, and non-transitory processor-readable storage medium for a source code file association system are provided herein. An example method includes receiving, by a project management tool associated with a software test lifecycle system, a pull request to merge software code changes with a software project repository on an enterprise system. The software testing life cycle system executes a plurality of integration test suites associated with the pull request on at least one test system associated with the enterprise system. The integration test suites are executed to test the software code changes. The project management tool captures the integration test suite output. A source code file association system preprocesses the integration test suite output for input into a machine learning system. The integration test suite output is encoded. The output of the machine learning system identifies at least a portion of source code exercised by the integration test suites.

Abstract: A method, computer program product, and computing system for generating a plurality of trained machine learning models using a cloud computing system by training a plurality of machine learning models to forecast storage performance for one or more storage objects of a storage system, wherein the cloud computing system is separate from any storage system. A trained machine learning model is selected from the plurality of trained machine learning models to deploy to a target storage system. The trained machine learning model is deployed on the target storage system.

Abstract: A method, computer program product, and computing system for processing historical input/output (IO) performance data associated with one or more storage objects of a storage system. A plurality of IO modeling systems may be trained using the historical IO performance data. Modeling performance information may be determined for the plurality of IO modeling systems across the historical IO performance data. A forecast score may be determined for each IO modeling system based on the modeling performance information for the plurality of IO modeling systems. A subset of the plurality of IO modeling systems may be selected based upon the forecast score for each IO modeling system. The at least one IO modeling system may be trained using the historical IO performance data. IO performance data may be forecasted using the at least one trained IO modeling system from the subset of the plurality of IO modeling systems.

Abstract: Techniques for grouping system logs using machine learning. The techniques include deriving an input matrix from the logs, in which rows represent the logs and columns represent unique words in the logs. The techniques include applying a TF-IDF algorithm to the input matrix and deriving a TF-IDF matrix from the TF-IDF algorithm output. The TF-IDF matrix reflects how important the unique words are to the respective logs. The techniques include applying a PCA algorithm to the TF-IDF matrix and deriving a PCA matrix having a reduced dimensionality from the PCA algorithm output. The techniques include applying a Cosine Similarity algorithm to the PCA matrix and deriving CS matrices from the CS algorithm output. Each CS matrix reflects the cosine similarity of a respective log relative to all the other logs. The techniques include applying a clustering algorithm to the CS matrices and deriving log groupings from the clustering algorithm output.

Abstract: Techniques for grouping system logs using machine learning. The techniques include deriving an input matrix from the logs, in which rows represent the logs and columns represent unique words in the logs. The techniques include applying a TF-IDF algorithm to the input matrix and deriving a TF-IDF matrix from the TF-IDF algorithm output. The TF-IDF matrix reflects how important the unique words are to the respective logs. The techniques include applying a PCA algorithm to the TF-IDF matrix and deriving a PCA matrix having a reduced dimensionality from the PCA algorithm output. The techniques include applying a Cosine Similarity algorithm to the PCA matrix and deriving CS matrices from the CS algorithm output. Each CS matrix reflects the cosine similarity of a respective log relative to all the other logs. The techniques include applying a clustering algorithm to the CS matrices and deriving log groupings from the clustering algorithm output.

Abstract: A method, computer program product, and computing system for processing historical input/output (IO) performance data associated with one or more storage objects of a storage system. A plurality of IO modeling systems may be trained using the historical IO performance data. Modeling performance information may be determined for the plurality of IO modeling systems across the historical IO performance data. A forecast score may be determined for each IO modeling system based on the modeling performance information for the plurality of IO modeling systems. A subset of the plurality of IO modeling systems may be selected based upon the forecast score for each IO modeling system. The at least one IO modeling system may be trained using the historical IO performance data. IO performance data may be forecasted using the at least one trained IO modeling system from the subset of the plurality of IO modeling systems.

Abstract: A method, computer program product, and computing system for processing historical input/output (IO) performance data associated with one or more storage objects of a storage system. A smoothing model may be applied on at least a portion of the historical IO performance data to generate forecast IO performance data. The forecast IO performance data may be compared to observed IO performance data to generate one or more performance differentials. A normal IO performance range may be generated based upon, at least in part, the one or more performance differentials. One or more IO performance anomalies may be detected based upon, at least in part, the normal IO performance range.