Multiple library dependency detection

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to:

• identify dependency relationships among libraries in a repository of libraries;
• train at least one machine learning model that predicts with a confidence value a dependency between a given library and a target library, training of the at least one machine learning model using the dependency relationships among libraries;
• create an L layer tree-like graph, using the dependency relationships among libraries and an application package, wherein L is configurable;
• and
• predict by the at least one machine learning model for each pair of nodes having a dependency relationship in the L layer tree-like graph, the dependency relationship with a confidence value, executing of the at least one machine learning model outputting versions of the libraries to use in the application package being programmed and compiled via the at least one computer processor, wherein pairs of nodes having largest confidence values are selected as the versions of the libraries to use in the application package, using the versions of the libraries in the application package reducing compilation errors caused by conflicting library codes in the application package from occurring on the at least one computer processor.

A system comprising:

• at least one processor;
• a memory device coupled with the at least one processor;
• the at least one processor configured at least to:
• identify dependency relationships among libraries in a repository of libraries;
• train at least one machine learning model that predicts with a confidence value a dependency between a given library and a target library, training of the at least one machine learning model using the dependency relationships among libraries;
• create an L layer tree-like graph, using the dependency relationships among libraries and an application package, wherein L is configurable;
• and
• predict by the at least one machine learning model for each pair of nodes having a dependency relationship in the L layer tree-like graph, the dependency relationship with a confidence value, the at least one machine learning model outputting versions of the libraries to use in the application package being programmed and compiled via the at least one computer processor, wherein pairs of nodes having largest confidence values are selected as the versions of the libraries to use in the application package, using the versions of the libraries in the application package reducing compilation errors caused by conflicting library codes in the application package from occurring on the at least one computer processor.