Abstract: Extensions to a source software application are migrated to a target. Template records are constructed containing corresponding sets of attributes for source and target applications. Analysis of a source extension yields source extension attributes. A matching template record is used to map the source extension attributes to corresponding target extension attributes. A proposal for a migrated target extension is formulated, simulated in a sandbox, and committed to the target application. Client interaction supports selection or approval of application versions, proposals, or simulation output. Template records, constructed without knowledge of specific extensions implemented on a particular application instance, are provided to the source application environment, enabling remaining migration operations to be performed locally in source and target environments, maintaining data privacy. Examples are provided for a front-end software application in a database environment.

Abstract: An improved system and process for machine-learning upgrade analysis and training thereof is provided herein. A request to analyze the time to upgrade a current system to a target system may be received. A change list having one or more changes for the target system may be read. Custom code for the current system may be compared to the change list to identify recommended changes to the custom code to upgrade the custom code to be compatible with the target system. The recommended changes may be classified into one or categories respectively via a trained first machine-learning algorithm. Time to upgrade the custom code for the respective classified changes may be estimated via a trained second machine-learning algorithm. The recommended changes, the classifications of the recommended changes, and the time estimates of the recommended changes may be provided.

Abstract: Systems and processes for validating data for reuse in a system are provided herein. An object type relating to a scenario of operations performed by a system of an organization may be identified. Database tables and characteristics related to an object corresponding to the object type, as well as an application programming interface (API) related to the object may be identified. Data in the database tables may be validated for reuse in the system using the API, and an indication of results of validating the data may be output to a user interface.

Abstract: Extensions to a source software application are migrated to a target. Template records are constructed containing corresponding sets of attributes for source and target applications. Analysis of a source extension yields source extension attributes. A matching template record is used to map the source extension attributes to corresponding target extension attributes. A proposal for a migrated target extension is formulated, simulated in a sandbox, and committed to the target application. Client interaction supports selection or approval of application versions, proposals, or simulation output. Template records, constructed without knowledge of specific extensions implemented on a particular application instance, are provided to the source application environment, enabling remaining migration operations to be performed locally in source and target environments, maintaining data privacy. Examples are provided for a front-end software application in a database environment.

Abstract: Provided are devices and methods for extending database views of a predefined data model to include custom industry fields, behind a switch. In one example, the method includes receiving a database table including common fields that are common to a plurality of industries and custom fields that are customized for at least one industry, generating a database view of the database table, the database view including the common fields transformed into a format represented by a predefined data model, executing an extension program that generates a data object in which the custom fields are transformed into the format represented by the predefined data model, and combining the data object including the custom fields having the transformed format with the database view including the common fields having the transformed format to generate an extended database view.

Abstract: An improved system and process for machine-learning upgrade analysis and training thereof is provided herein. A request to analyze the time to upgrade a current system to a target system may be received. A change list having one or more changes for the target system may be read. Custom code for the current system may be compared to the change list to identify recommended changes to the custom code to upgrade the custom code to be compatible with the target system. The recommended changes may be classified into one or categories respectively via a trained first machine-learning algorithm. Time to upgrade the custom code for the respective classified changes may be estimated via a trained second machine-learning algorithm. The recommended changes, the classifications of the recommended changes, and the time estimates of the recommended changes may be provided.

Abstract: Provided are devices and methods for extending database views of a predefined data model to include custom industry fields, behind a switch. In one example, the method includes receiving a database table including common fields that are common to a plurality of industries and custom fields that are customized for at least one industry, generating a database view of the database table, the database view including the common fields transformed into a format represented by a predefined data model, executing an extension program that generates a data object in which the custom fields are transformed into the format represented by the predefined data model, and combining the data object including the custom fields having the transformed format with the database view including the common fields having the transformed format to generate an extended database view.