# Import2vec - Learning Embeddings for Software Libraries

arXiv: https://arxiv.org/abs/1904.03990 · HTML: https://mlsanity.com/paper/1904.03990/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [On the validity of pre-trained transformers for natural language processing in the software engineering domain](https://mlsanity.com/paper/2109.04738/) (2021)
- [Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection](https://mlsanity.com/paper/2206.08726/) (2022)
- [Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity](https://mlsanity.com/paper/2107.05112/) (2021)
- [Dev2vec: Representing Domain Expertise of Developers in an Embedding Space](https://mlsanity.com/paper/2207.05132/) (2022)
- [Antipatterns in Software Classification Taxonomies](https://mlsanity.com/paper/2204.08880/) (2022)
- [Code Librarian: A Software Package Recommendation System](https://mlsanity.com/paper/2210.05406/) (2022)
- [Topical: Learning Repository Embeddings from Source Code using Attention](https://mlsanity.com/paper/2208.09495/) (2022)

## Abstract (arXiv)

We consider the problem of developing suitable learning representations (embeddings) for library packages that capture semantic similarity among libraries. Such representations are known to improve the performance of downstream learning tasks (e.g. classification) or applications such as contextual search and analogical reasoning. We apply word embedding techniques from natural language processing (NLP) to train embeddings for library packages ("library vectors"). Library vectors represent libraries by similar context of use as determined by import statements present in source code. Experimental results obtained from training such embeddings on three large open source software corpora reveals that library vectors capture semantically meaningful relationships among software libraries, such as the relationship between frameworks and their plug-ins and libraries commonly used together within ecosystems such as big data infrastructure projects (in Java), front-end and back-end web development frameworks (in JavaScript) and data science toolkits (in Python).