Extensibility recommendation system for custom code objects

1. A method, comprising:

receiving, by a device, extensibility data related to one or more custom code objects installed in a current environment,

wherein the extensibility data includes information associated with a list of the one or more custom code objects, and

wherein the one or more custom code objects are cloned from standard objects associated with a software system related to the current environment;

classifying, by the device, the one or more custom code objects installed in the current environment in one or more respective categories based on the extensibility data associated with the one or more custom code objects;

determining, by the device, one or more respective complexities associated with the one or more custom code objects installed in the current environment based on the extensibility data associated with the one or more custom code objects;

generating, by the device, an extensibility recommendation for deploying the one or more custom code objects to a target environment based on the one or more respective categories and the one or more respective complexities associated with the one or more custom code objects,

wherein the extensibility recommendation is generated based on a determination that the one or more custom code objects satisfy one or more extensibility conditions associated with the target environment; and

providing, by the device, an output that indicates the extensibility recommendation for deploying the one or more custom code objects to the target environment.

2. The method of claim 1 , wherein the extensibility data related to the one or more custom code objects is received from a data extraction utility that is installed in the current environment and configured to scan the one or more custom code objects.

3. The method of claim 2 , wherein the extensibility data received from the data extraction utility includes, for each of the one or more custom code objects, one or more metadata fields that describe the custom code object and one or more metadata fields that describe:

category-specific tokens associated with the custom code object,

parameters related to a complexity associated with the custom code object, or

parameters related to an extensibility associated with the custom code object.

4. The method of claim 2 , wherein the extensibility data received from the data extraction utility includes usage data related to one or more of a frequency or a volume of use for each custom code object, and wherein the extensibility recommendation is based on the usage data.

5. The method of claim 2 , wherein the extensibility data received from the data extraction utility includes clone analytics related to each custom code object that has been cloned from a standard object, and wherein the extensibility recommendation is based on the clone analytics.

6. The method of claim 1 , further comprising:

receiving questionnaire responses related to the one or more custom code objects installed in the current environment; and

providing the questionnaire responses to a machine learning model that is trained to predict the extensibility data related to the one or more custom code objects using historical extensibility recommendations and historical extensibility data, wherein the extensibility data related to the one or more custom code objects is received from the machine learning model.

7. The method of claim 1 , further comprising:

providing, to a machine learning system, training data that includes historical extensibility recommendations and historical extensibility data;

determining that the extensibility recommendation for deploying the one or more custom code objects to the target environment relates to a scenario that is not represented in the training data provided to the machine learning system; and

performing, based on information related to the scenario, an incremental learning process to update a machine learning model that was trained using the training data.

8. The method of claim 1 , further comprising:

providing the extensibility data associated with the one or more custom code objects to a machine learning system configured to provide an output that includes enrichment data associated with the extensibility recommendation; and

optimizing, based on the enrichment data, the output that indicates the extensibility recommendation for deploying the one or more custom code objects to the target environment.

9. The method of claim 1 , wherein the output that indicates the extensibility recommendation for deploying the one or more custom code objects includes information related to a standard fitment, a standardization feasibility, an estimated effort, or a recommended solution approach for deploying the one or more custom code objects to the target environment.

10. The method of claim 1 , wherein the extensibility recommendation indicates a side-by-side extensibility, an in-app extensibility, a classic extensibility, or a hyperscaler extensibility for the one or more custom code objects that satisfy the one or more extensibility conditions.

11. A device, comprising:

one or more memories; and

one or more processors, coupled to the one or more memories, configured to:

deploy, to a current environment, a data extraction utility configured to scan one or more custom code objects installed in the current environment;

receive, from the data extraction utility, extensibility data related to the one or more custom code objects installed in the current environment,

wherein the extensibility data includes information associated with a list of the one or more custom code objects,

wherein the one or more custom code objects are cloned from standard objects associated with a software system related to the current environment,

wherein the extensibility data further includes, for each of the one or more custom code objects, one or more metadata fields that describe the custom code object and one or more metadata fields that describe one or more of:

category-specific tokens associated with the custom code object,

parameters related to a complexity associated with the custom code object, or

parameters related to an extensibility associated with the custom code object;

determine one or more respective categories and one or more respective complexities associated with the one or more custom code objects installed in the current environment based on the extensibility data;

generate an extensibility recommendation for deploying the one or more custom code objects to a target environment based on the one or more respective categories and the one or more respective complexities associated with the one or more custom code objects,

wherein the extensibility recommendation is generated based on the one or more custom code objects satisfying one or more extensibility conditions associated with the target environment; and

provide an output that indicates the extensibility recommendation, of the one or more extensibility conditions, for deploying the one or more custom code objects to the target environment.

12. The device of claim 11 , wherein the extensibility data received from the data extraction utility includes usage data related to one or more of a frequency or a volume of use for each custom code object, and wherein the extensibility recommendation is based on the usage data.

13. The device of claim 11 , wherein the extensibility data received from the data extraction utility includes clone analytics related to each custom code object that has been cloned from a standard object, and wherein the extensibility recommendation is based on the clone analytics.

14. The device of claim 11 , wherein the one or more processors are further configured to:

provide, to a machine learning system, training data that includes historical extensibility recommendations and historical extensibility data;

determine that the extensibility recommendation for deploying the one or more custom code objects to the target environment relates to a scenario that is not represented in the training data provided to the machine learning system; and

perform, based on information related to the scenario, an incremental learning process to update a machine learning model that was trained using the training data.

15. The device of claim 11 , wherein the one or more processors are further configured to:

provide the extensibility data associated with the one or more custom code objects to a machine learning system configured to provide an output that includes enrichment data associated with the extensibility recommendation; and

optimize, based on the enrichment data, the output that indicates the extensibility recommendation for deploying the one or more custom code objects to the target environment.

16. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

one or more instructions that, when executed by one or more processors of a device, cause the device to:

receive extensibility data related to one or more custom code objects installed in a current environment,

wherein the extensibility data includes information associated with the one or more custom code objects, and

wherein the one or more custom code objects are cloned from standard objects associated with a software system related to the current environment;

generate an extensibility recommendation for deploying the one or more custom code objects to a target environment based on one or more respective categories and one or more respective complexities that are associated with the one or more custom code objects based on the extensibility data; and

provide an output that indicates the extensibility recommendation for deploying the one or more custom code objects to the target environment based on the one or more custom code objects satisfying one or more extensibility conditions associated with the target environment,

wherein the output that indicates the extensibility recommendation for deploying the one or more custom code objects includes information related to a standard fitment, a standardization feasibility, an estimated effort, or a recommended solution approach for deploying the one or more custom code objects to the target environment, and

wherein the extensibility recommendation indicates a side-by-side extensibility, an in-app extensibility, a classic extensibility, or a hyperscaler extensibility for the one or more custom code objects that satisfy the one or more extensibility conditions.

17. The non-transitory computer-readable medium of claim 16 , wherein the extensibility data related to the one or more custom code objects is received from a data extraction utility that is installed in the current environment and configured to scan the one or more custom code objects.

18. The non-transitory computer-readable medium of claim 16 , wherein the one or more instructions further cause the device to:

receive questionnaire responses related to the one or more custom code objects installed in the current environment; and

provide the questionnaire responses to a machine learning model that is trained to predict the extensibility data related to the one or more custom code objects using historical extensibility recommendations and historical extensibility data, wherein the extensibility data related to the one or more custom code objects is received from the machine learning model.

19. The non-transitory computer-readable medium of claim 16 , wherein the one or more instructions further cause the device to:

provide, to a machine learning system, training data that includes historical extensibility recommendations and historical extensibility data;

determine that the extensibility recommendation for deploying the one or more custom code objects to the target environment relates to a scenario that is not represented in the training data provided to the machine learning system; and

perform, based on information related to the scenario, an incremental learning process to update a machine learning model that was trained using the training data.

20. The non-transitory computer-readable medium of claim 16 , wherein the one or more instructions further cause the device to:

provide the extensibility data associated with the one or more custom code objects to a machine learning system configured to provide an output that includes enrichment data associated with the extensibility recommendation; and

optimize, based on the enrichment data, the output that indicates the extensibility recommendation for deploying the one or more custom code objects to the target environment.