Abstract: Various embodiments relate to data science and data analysis, computer software and systems, and computing architectures and data models configured to facilitate management of enterprise functions, and, more specifically, to an enterprise computing and data processing platform configured to activate risk management transformations of enterprise data in-situ, responsive to identifying a risk event, and further configured to implement a risk management data channel to facilitate analyses and responses associated with an enterprise computing device. In some examples, a method may include receiving a risk data signal, identifying a portion of the risk data signal, computing data representing a risk level, classifying data associated with a hierarchical business data object in accordance with a risk level, aggregating classified data with other data associated with other business data objects classified as a function of risk to form aggregated data, causing presentation of aggregated data as a function of risk.

Abstract: A technique is described for evaluating code at a local computing device before deploying the code to a cloud computing platform to be compiled. In an example embodiment, class files including the code in a programming language associated with the cloud computing environment are loaded by a local computer system, for example, associated with a software developer. The local computer system then parses the code to identify elements in the code and checks the identified elements. Errors in the code are identified based on the checking and are displayed to a user (e.g., the developer), for example, via a graphical user interface of a code editor application.

Abstract: A technique is described for evaluating code at a local computing device before deploying the code to a cloud computing platform to be compiled. In an example embodiment, class files including the code in a programming language associated with the cloud computing environment are loaded by a local computer system, for example, associated with a software developer. The local computer system then parses the code to identify elements in the code and checks the identified elements. Errors in the code are identified based on the checking and are displayed to a user (e.g., the developer), for example, via a graphical user interface of a code editor application.

Abstract: A technique is described for evaluating code at a local computing device before deploying the code to a cloud computing platform to be compiled. In an example embodiment, class files including the code in a programming language associated with the cloud computing environment are loaded by a local computer system, for example, associated with a software developer. The local computer system then parses the code to identify elements in the code and checks the identified elements. Errors in the code are identified based on the checking and are displayed to a user (e.g., the developer), for example, via a graphical user interface of a code editor application.

Abstract: Various embodiments relate to data science and data analysis, computer software and systems, and computing architectures and data models configured to facilitate management of enterprise functions, and, more specifically, to an enterprise computing and data processing platform configured to activate risk management transformations of enterprise data in-situ, responsive to identifying a risk event, and further configured to implement a risk management data channel to facilitate analyses and responses associated with an enterprise computing device. In some examples, a method may include receiving a risk data signal, identifying a portion of the risk data signal, computing data representing a risk level, classifying data associated with a hierarchical business data object in accordance with a risk level, aggregating classified data with other data associated with other business data objects classified as a function of risk to form aggregated data, causing presentation of aggregated data as a function of risk.

Abstract: A technique is described for evaluating code at a local computing device before deploying the code to a cloud computing platform to be compiled. In an example embodiment, class files including the code in a programming language associated with the cloud computing environment are loaded by a local computer system, for example, associated with a software developer. The local computer system then parses the code to identify elements in the code and checks the identified elements. Errors in the code are identified based on the checking and are displayed to a user (e.g., the developer), for example, via a graphical user interface of a code editor application.

Abstract: An item to be processed is received and scanned. The scan identifies any functions or procedures in the item being processed, and extracts actual code representing those functions or procedures. Any invoking functions, which invoke the identified function, are, themselves, identified. Actual code for the invoking function is extracted as well. An output documentation file is generated that includes a name of the identified function or procedure, as well as actual code for the function or procedure, and actual code from at least one example of an invoking function.

Abstract: An item to be processed is received and scanned. The scan identifies any functions or procedures in the item being processed, and extracts actual code representing those functions or procedures. Any invoking functions, which invoke the identified function, are, themselves, identified. Actual code for the invoking function is extracted as well. An output documentation file is generated that includes a name of the identified function or procedure, as well as actual code for the function or procedure, and actual code from at least one example of an invoking function.

Abstract: A data source, such as a database, is modeled using state variables, state, and actions performed on the data source. State variable queries may be automatically generated by generating queries or functions based on intercepting data queries to a data source and using the intercepted data query language. State variable queries may automatically inspect data objects within the data store and automatically generate state variable values. The state variable queries or functions may be executed automatically after an action is run against a data source such that state information is automatically determined. Further, a graphical user interface used to modify, add and delete state information and automatic state variable queries associated with the verification model is described.

Abstract: A data source, such as a database, is modeled using state variables, state, and actions performed on the data source. State variable queries may be automatically generated by generating queries or functions based on intercepting data queries to a data source and using the intercepted data query language. State variable queries may automatically inspect data objects within the data store and automatically generate state variable values. The state variable queries or functions may be executed automatically after an action is run against a data source such that state information is automatically determined. Further, a graphical user interface used to modify, add and delete state information and automatic state variable queries associated with the verification model is described.