Intelligent application library management

1. A method, by a processor, for providing intelligent library management in a computing environment, comprising:

learning, using a machine learning component, application compatibility from one or more application projects, repositories, a plurality of application libraries, data sources, or a combination thereof;

automatically providing a list of predicted application libraries, ordered according to the application compatibility of an application-pair for implementation, integration, or replacement of one or more sections of an application library, wherein the application-pair is identified according to a compatibility model trained from the learned application compatibility, wherein the list of predicted application libraries is ranked according to the application compatibility for implementation, integration, or replacement of one or more sections of one or more application projects, and wherein the providing of the list of predicted application libraries is performed inclusively for a previously unknown application-pair; and

collecting feedback, using the machine learning component, to perform the learning of the application compatibility of the one or more application projects from the plurality of application libraries, the data sources, or a combination thereof.

2. The method of claim 1 , further including searching or crawling the one or more application projects, repositories, a plurality of application libraries, data sources, or a combination thereof to identify and learn content therein.

3. The method of claim 1 , wherein the list of predicted application libraries is identified from the one or more application projects, repositories, the plurality of application libraries, data sources, or a combination thereof to add or modify the one or more sections of the one or more application projects.

4. The method of claim 1 , further including indicating those of the list of predicted application libraries being incompatible with one or more application projects.

5. The method of claim 1 , further including:

retrieving, from the plurality of application libraries, those application libraries on the list of predicted application libraries, metadata, or a combination thereof; and

creating a semantic index for the list of predicted application libraries.

6. The method of claim 1 , further including providing the list of predicted application libraries matching a defined query.

7. A system for providing intelligent library management in a computing environment, comprising:

one or more computers with executable instructions that when executed cause the system to:

learn, using a machine learning component, application compatibility from one or more application projects, repositories, a plurality of application libraries, data sources, or a combination thereof;

automatically provide a list of predicted application libraries, ordered according to the application compatibility of an application-pair for implementation, integration, or replacement of one or more sections of an application library, wherein the application-pair is identified according to a compatibility model trained from the learned application compatibility, wherein the list of predicted application libraries is ranked according to the application compatibility for implementation, integration, or replacement of one or more sections of one or more application projects, and wherein the providing of the list of predicted application libraries is performed inclusively for a previously unknown application-pair; and

collect feedback, using the machine learning component, to perform the learning of the application compatibility of the one or more application projects from the plurality of application libraries, the data sources, or a combination thereof.

8. The system of claim 7 , wherein the executable instructions when executed cause the system to search or crawl the one or more application projects, repositories, the plurality of application libraries, data sources, or a combination thereof to identify and learn content therein.

9. The system of claim 7 , wherein the list of the predicted application libraries is identified from the one or more application projects, repositories, the plurality of application libraries, data sources, or a combination thereof to add or modify the one or more sections of the one or more application projects.

10. The system of claim 7 , wherein the executable instructions when executed cause the system to indicate those of the list of predicted application libraries being incompatible with one or more application projects.

11. The system of claim 7 , wherein the executable instructions when executed cause the system to:

retrieve, from the plurality of application libraries, those application libraries on the list of predicted application libraries, metadata, or a combination thereof; and

create a semantic index for the list of predicted application libraries.

12. The system of claim 7 , wherein the executable instructions when executed cause the system to provide the list of predicted application libraries matching a defined query.

13. A computer program product for providing intelligent library management by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

an executable portion, executed by the processor, that uses a machine learning component to learn application compatibility from one or more application projects, repositories, a plurality of application libraries, data sources, or a combination thereof;

an executable portion, executed by the processor, that automatically provides a list of predicted application libraries, ordered according to the application compatibility of an application-pair for implementation, integration, or replacement of one or more sections of an application library, wherein the application-pair is identified according to a compatibility model trained from the learned application compatibility, wherein the list of predicted application libraries is ranked according to the application compatibility for implementation, integration, or replacement of one or more sections of one or more application projects, and wherein the providing of the list of predicted application libraries is performed inclusively for a previously unknown application-pair; and

an executable portion, executed by the processor, that uses the machine learning component to collect feedback to perform the learning of the application compatibility of the one or more application projects from the plurality of application libraries, the data sources, or a combination thereof.

14. The computer program product of claim 13 , further including an executable portion, executed by the processor, that:

searches or crawls the one or more application projects, repositories, the plurality of application libraries, data sources, or a combination thereof to identify and learn content therein;

wherein the list of predicted application libraries is identified from the one or more application projects, the repositories, the plurality of application libraries, the data sources, or the combination thereof to add or modify the one or more sections of the one or more application projects.

15. The computer program product of claim 13 , further including an executable portion, executed by the processor, that indicates those of the list of predicted application libraries being incompatible with one or more application projects.

16. The computer program product of claim 13 , further including an executable portion, executed by the processor, that:

retrieves, from the plurality of application libraries, those application libraries on the list of predicted application libraries, metadata, or a combination thereof; and

creates a semantic index for the list of predicted application libraries.

17. The computer program product of claim 13 , further including an executable portion, executed by the processor, that provides the list of predicted application libraries matching a defined query.