# Code Librarian: A Software Package Recommendation System

arXiv: https://arxiv.org/abs/2210.05406 · HTML: https://mlsanity.com/paper/2210.05406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Import2vec - Learning Embeddings for Software Libraries](https://mlsanity.com/paper/1904.03990/) (2019)
- [Req2Lib: A Semantic Neural Model for Software Library Recommendation](https://mlsanity.com/paper/2005.11757/) (2020)

## Built on by (most cited first, 1 of 1)

- [AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agents](https://mlsanity.com/paper/2510.03495/) (2025)

## Abstract (arXiv)

The use of packaged libraries can significantly shorten the software development cycle by improving the quality and readability of code. In this paper, we present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code. We apply the state-of-the-art CodeBERT-based model for analysing the context of the source code to deliver relevant library recommendations to users.