Fast Library Recommendation in Software Dependency Graphs with Symmetric Partially Absorbing Random Walks

Abstract

To help developers discover libraries suited to their software projects, automated approaches often start from already employed libraries and recommend more based on co-occurrence patterns in other projects. The most accurate project–library recommendation systems employ Graph Neural Networks (GNNs) that learn latent node representations for link prediction. However, GNNs need to be retrained when dependency graphs are updated, for example, to recommend libraries for new projects, and are thus unwieldy for scalable deployment. To avoid retraining, we propose that recommendations can instead be performed with graph filters; by analyzing dependency graph dynamics emulating human-driven library discovery, we identify low-pass filtering with memory as a promising direction and introduce a novel filter, called symmetric partially absorbing random walks, which infers rather than trains the parameters of filters with node-specific memory to guarantee low-pass filtering. Experiments on a dependency graph between Android projects and third-party libraries show that our approach makes recommendations with a quality and diversification loosely comparable to those state-of-the-art GNNs without computationally intensive retraining for new predictions.