公开(公告)号：US12204917B2

公开(公告)日：2025-01-21

申请号：US18065877

申请日：2022-12-14

Applicant: SAP SE

Inventor： Garima Srivastava ,  Sushama Shadakshary

IPC: G06F9/445 ,  G06F8/36 ,  G06F8/41

Abstract: An automated extensibility framework is provided to automatically convert or migrate application extensions from a source system to be used on target applications in a target format. Extension artefacts of an extension of a source application on a source system are obtained. Each of the extension artefacts are parsed into a target format of a target platform based on a type of the corresponding extension artefact. Target applications of the target platform are identified based on an identifier of the source application. Extension simulations are compiled for each of the one or more target applications. Then, a user interface is provided which enables a user to select simulations from among extension simulations. Then the selected simulations are published to the target platform such that the corresponding extensions are implemented in corresponding target applications.

公开(公告)号：US20240202012A1

公开(公告)日：2024-06-20

申请号：US18065877

申请日：2022-12-14

Applicant: SAP SE

Inventor： Garima Srivastava ,  Sushama Shadakshary

IPC: G06F9/445 ,  G06F8/36 ,  G06F8/41

CPC classification number: G06F9/44526 ,  G06F8/36 ,  G06F8/427

Abstract: An automated extensibility framework is provided to automatically convert or migrate application extensions from a source system to be used on target applications in a target format. Extension artefacts of an extension of a source application on a source system are obtained. Each of the extension artefacts are parsed into a target format of a target platform based on a type of the corresponding extension artefact. Target applications of the target platform are identified based on an identifier of the source application. Extension simulations are compiled for each of the one or more target applications. Then, a user interface is provided which enables a user to select simulations from among extension simulations. Then the selected simulations are published to the target platform such that the corresponding extensions are implemented in corresponding target applications.

公开(公告)号：US10983782B1

公开(公告)日：2021-04-20

申请号：US16778486

申请日：2020-01-31

Applicant: SAP SE

Inventor： Paulami Mitra ,  Garima Srivastava

IPC: G06F8/65 ,  G06F8/71 ,  G06F9/451 ,  G06F3/0482

Abstract: Systems and methods provide determination of a first user interface application associated with a first version of user interface code libraries, and reception of a request to analyze compatibility of the first user interface application with a second version of user interface code libraries. In response to the request the second version of user interface code libraries is retrieved, it is determined whether one or more user interface code libraries referenced in the first user interface application are not in the second version of user interface code libraries, and, if it is determined that one or more user interface code libraries referenced in the first user interface application are not in the second version of user interface code libraries, a message is presented indicating that one or more user interface code libraries referenced in the first user interface application are not in the second version of user interface code libraries.

公开(公告)号：US20200012970A1

公开(公告)日：2020-01-09

申请号：US16028747

申请日：2018-07-06

Applicant: SAP SE

Inventor： Garima Srivastava ,  Yeshwant More

IPC: G06N99/00 ,  G06F17/30

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.