Neural Library Recommendation by Embedding Project-Library Knowledge Graph

doi:10.1109/TSE.2024.3393504

Neural Library Recommendation by Embedding Project-Library Knowledge Graph

2024-06-01

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DOI：10.1109/TSE.2024.3393504

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The prosperity of software applications brings fierce market competition to developers. Employing third-party libraries (TPLs) to add new features to projects under development and to reduce the time to market has become a popular way in the community. However, given the tremendous TPLs ready for use, it is challenging for developers to effectively and efficiently identify the most suitable TPLs. To tackle this obstacle, we propose an innovative approach named PyRec to recommend potentially useful TPLs to developers for their projects. Taking Python project development as a use case, PyRec embeds Python projects, TPLs, contextual information, and relations between those entities into a knowledge graph. Then, it employs a graph neural network to capture useful information from the graph to make TPL recommendations. Different from existing approaches, PyRec can make full use of not only project-library interaction information but also contextual information to make more accurate TPL recommendations. Comprehensive evaluations are conducted based on 12,421 Python projects involving 963 TPLs, 9,675 extra entities, 121,474 library usage records, and 73,277 contextual records. Compared with five representative approaches, PyRec improves the recommendation performance significantly in all cases.

Keywords:

Python

Libraries

Vectors

Software

Mobile applications

Knowledge graphs

Graph neural networks

Third-party library

recommendation

knowledge graph

graph neural network

Neural Library Recommendation by Embedding Project-Library Knowledge Graph

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