17943398. MULTIPLE LIBRARY DEPENDENCY DETECTION simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
MULTIPLE LIBRARY DEPENDENCY DETECTION
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
MULTIPLE LIBRARY DEPENDENCY DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17943398 titled 'MULTIPLE LIBRARY DEPENDENCY DETECTION
Simplified Explanation
The patent application describes a method for predicting dependency relationships among libraries in a repository using machine learning models and creating a tree-like graph to determine library versions for an application package.
- Identification of dependency relationships among libraries in a repository
- Creation of a machine learning model to predict dependencies with confidence values
- Generation of an L-layer tree-like graph using dependency relationships
- Determination of library versions for an application package based on confidence values
- Selection of library versions with the largest confidence values for use in the application package
Potential Applications
This technology can be applied in software development to streamline the process of managing library dependencies and selecting appropriate versions for application packages.
Problems Solved
This technology solves the problem of manual identification and selection of library dependencies, providing a more efficient and accurate method for determining library versions.
Benefits
The benefits of this technology include improved accuracy in selecting library versions, increased efficiency in managing dependencies, and enhanced overall performance of application packages.
Potential Commercial Applications
- Optimizing software development processes
- Enhancing application performance through efficient library version selection
- Streamlining dependency management in software projects
Original Abstract Submitted
At least one processor identifies dependency relationships among libraries in a repository of libraries. Using the dependency relationships among libraries, at least one machine learning model can be created that predicts with a confidence value a dependency between a given library and a target library. An L layer tree-like graph can be created, using the dependency relationships among libraries and an application package. L can be configurable. Versions of the libraries to use can be determined by running the at least one machine learning model for each pair of nodes having a dependency relationship in the L layer tree-like graph, the at least one machine learning model identifying the dependency relationship with a confidence value, where pairs of nodes having largest confidence values are selected as the versions of the libraries to use in the application package.
