Req2Lib: A Semantic Neural Model for Software Library Recommendation

TL;DR

Req2Lib is a neural network-based system that recommends software libraries based on natural language requirement descriptions, effectively addressing the cold-start problem and improving recommendation accuracy.

Contribution

The paper introduces Req2Lib, a novel neural model utilizing domain-specific embeddings and sequence-to-sequence learning for library recommendation from natural language requirements.

Findings

Achieved accurate library recommendations in experiments with 5,625 Java projects.

Utilized domain-specific word embeddings trained on Stack Overflow data.

Demonstrated effectiveness in matching user requirements to suitable libraries.

Abstract

Third-party libraries are crucial to the development of software projects. To get suitable libraries, developers need to search through millions of libraries by filtering, evaluating, and comparing. The vast number of libraries places a barrier for programmers to locate appropriate ones. To help developers, researchers have proposed automated approaches to recommend libraries based on library usage pattern. However, these prior studies can not sufficiently match user requirements and suffer from cold-start problem. In this work, we would like to make recommendations based on requirement descriptions to avoid these problems. To this end, we propose a novel neural approach called Req2Lib which recommends libraries given descriptions of the project requirement. We use a Sequence-to-Sequence model to learn the library linked-usage information and semantic information of requirement descriptions in natural language. Besides, we apply a domain-specific pre-trained word2vec model for word embedding, which is trained over textual corpus from Stack Overflow posts. In the experiment, we train and evaluate the model with data from 5,625 java projects. Our preliminary evaluation demonstrates that Req2Lib can recommend libraries accurately.