Abstract: A system and method for automatic coding out biases in applications, systems, and processes are disclosed. A processor operatively connected to a memory via a communication interface applies an intake process based on received inventory data to applications, systems, and processes and implements a machine learning model in response to applying the intake process. The processor also identifies areas of the potential bias data within the applications, systems, and processes by utilizing the machine learning model based on analyzing response data received during the intake process; generates output data that includes bias data and exceptions data identified for the applications, systems, and processes; and mitigates the bias data and exceptions data in response to the output data by implementing a mitigation process.

Abstract: A system and method for automatic coding out biases in applications, systems, and processes are disclosed. A processor operatively connected to a memory via a communication interface applies an intake process based on received inventory data to applications, systems, and processes and implements a machine learning model in response to applying the intake process. The processor also identifies areas of the potential bias data within the applications, systems, and processes by utilizing the machine learning model based on analyzing response data received during the intake process; generates output data that includes bias data and exceptions data identified for the applications, systems, and processes; and mitigates the bias data and exceptions data in response to the output data by implementing a mitigation process.

Abstract: Various methods, apparatuses/systems, and media for implementing an automation testing module are disclosed. A processor creates a plurality of production robots each configured to validate a particular nonfunctional requirement (NFR) among a plurality of NFRs of an application during a development environment of the application. The processor identifies a tool specific for testing the particular NFR from the plurality of production robots; and implements the identified tool's application programming interface (API) to automatically execute a test scenario to validate the particular NFR. The test scenario is selected from a plurality of test scenarios to be executed and tested by the production robots to validate each NFR during the development phase and to determine that the application is stable and ready for production based on validation of the plurality of NFRs prior to entering into a production phase of the application.

Abstract: Various methods, apparatuses/systems, and media for implementing an automation testing module are disclosed. A processor creates a plurality of production robots each configured to validate a particular nonfunctional requirement (NFR) among a plurality of NFRs of an application during a development environment of the application. The processor identifies a tool specific for testing the particular NFR from the plurality of production robots; and implements the identified tool's application programming interface (API) to automatically execute a test scenario to validate the particular NFR. The test scenario is selected from a plurality of test scenarios to be executed and tested by the production robots to validate each NFR during the development phase and to determine that the application is stable and ready for production based on validation of the plurality of NFRs prior to entering into a production phase of the application.

Abstract: Various methods, apparatuses/systems, and media for implementing an automation testing module are disclosed. A processor creates a plurality of production robots each configured to validate a particular nonfunctional requirement (NFR) among a plurality of NFRs of an application during a development environment of the application. The processor identifies a tool specific for testing the particular NFR from the plurality of production robots; and implements the identified tool's application programming interface (API) to automatically execute a test scenario to validate the particular NFR. The test scenario is selected from a plurality of test scenarios to be executed and tested by the production robots to validate each NFR during the development phase and to determine that the application is stable and ready for production based on validation of the plurality of NFRs prior to entering into a production phase of the application.

Abstract: Various methods, apparatuses/systems, and media for implementing an automation testing module are disclosed. A processor creates a plurality of production robots each configured to validate a particular nonfunctional requirement (NFR) among a plurality of NFRs of an application during a development environment of the application. The processor identifies a tool specific for testing the particular NFR from the plurality of production robots; and implements the identified tool's application programming interface (API) to automatically execute a test scenario to validate the particular NFR. The test scenario is selected from a plurality of test scenarios to be executed and tested by the production robots to validate each NFR during the development phase and to determine that the application is stable and ready for production based on validation of the plurality of NFRs prior to entering into a production phase of the application.