Recommending Analogical APIs via Knowledge Graph Embedding

TL;DR

This paper introduces KGE4AR, a knowledge graph embedding approach for recommending analogical APIs during library migration, significantly improving accuracy and scalability over existing documentation-based methods.

Contribution

KGE4AR constructs a unified API knowledge graph and embeds it into vectors, enhancing semantic understanding and scalability for API recommendation tasks.

Findings

KGE4AR outperforms state-of-the-art techniques in all evaluation metrics.

It achieves 47.1%-143.0% and 11.7%-80.6% improvements in MRR.

The approach scales effectively with increasing library numbers.

Abstract

Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One essential part of library migration is to find an analogical API that could provide the same functionality as current ones. However, given the large number of libraries/APIs, manually finding an analogical API could be very time-consuming and error-prone. Researchers have developed multiple automated analogical API recommendation techniques. Documentation-based methods have particularly attracted significant interest. Despite their potential, these methods have limitations, such as a lack of comprehensive semantic understanding in documentation and scalability challenges. In this work, we propose KGE4AR, a novel documentation-based approach that leverages knowledge graph (KG) embedding to recommend analogical APIs during library migration. Specifically, KGE4AR proposes a novel unified API KG to comprehensively and structurally represent three types of knowledge in documentation, which can better capture the high-level semantics. Moreover, KGE4AR then proposes to embed the unified API KG into vectors, enabling more effective and scalable similarity calculation. We build KGE4AR' s unified API KG for 35,773 Java libraries and assess it in two API recommendation scenarios: with and without target libraries. Our results show that KGE4AR substantially outperforms state-of-the-art documentation-based techniques in both evaluation scenarios in terms of all metrics (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements in each scenario). Additionally, we explore KGE4AR' s scalability, confirming its effective scaling with the growing number of libraries.