Abstract: As described herein, a system, method, and computer program are provided for generative AI automation flows. Input describing a new requirement for an application is processed, using generative artificial intelligence, to generate one or more test cases for the application. Key words of the one or more test cases are determined. A tokenized dictionary is built using the key words. User interface components of the application are discovered. One or more automation scripts are automatically generated for each of the user interface components of the application. A machine learning model is used to build an end-to-end automation flow for testing the application, wherein the end-to-end automation flow connects the one or more automation scripts generated for each of the user interface components of the application to the one or more test cases generated for the application. The end-to-end automation flow is executed for testing the application.

Abstract: As described herein, a system, method, and computer program are provided for multi-channel application testing using generative AI. A test automation flow generated for an application is accessed. Generative artificial intelligence (AI) is used to automatically adapt the test automation flow to a plurality of versions of the application corresponding to different channels. The plurality of versions of the application are tested using the adapted test automation flows.

Abstract: As described herein, a system, method, and computer program are provided for test-related automatic tracing. A new requirement defined for an application is identified. The new requirement is processed, using a machine learning model, to predict each portion of the new requirement covered by existing test features. One or more new test features are caused to be created for the new requirement, based on the prediction of each portion of the new requirement covered by existing test features.

Abstract: As described herein, a system, method, and computer program are provided for defect resolution. Information associated with a defect detected in a computer system is received. The information is processed, using a first machine learning model, to predict a source of the defect. The information and the source of the defect are processed, using a second machine learning model, to predict one or more parameters for handling the defect. One or more actions are caused to be performed to resolve the defect, based on the predicted one or more parameters for handling the defect.

Abstract: As described herein, a system, method, and computer program are provided for intelligent value stream management. In use, a questionnaire is generated for a plurality of customers of an organization, wherein the organization provides a platform running a plurality of services for use by the customers. The plurality of customers are provided with access to the questionnaire. Answers to the questionnaire are received from at least one customer of the plurality of customers. Data associated with actual usage of the plurality of services is collected. The answers received from the at least one customer and the collected data are processed, using a machine learning model, to predict at least one change to be made with respect to the plurality of services.