# Dev2vec: Representing Domain Expertise of Developers in an Embedding Space

arXiv: https://arxiv.org/abs/2207.05132 · HTML: https://mlsanity.com/paper/2207.05132/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [Commit2Vec: Learning Distributed Representations of Code Changes](https://mlsanity.com/paper/1911.07605/) (2019)
- [Import2vec - Learning Embeddings for Software Libraries](https://mlsanity.com/paper/1904.03990/) (2019)
- [Representation of Developer Expertise in Open Source Software](https://mlsanity.com/paper/2005.10176/) (2020)
- [Identifying Experts in Software Libraries and Frameworks among GitHub Users](https://mlsanity.com/paper/1903.08113/) (2019)
- [Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning](https://mlsanity.com/paper/2009.00331/) (2020)
- [Towards Mining OSS Skills from GitHub Activity](https://mlsanity.com/paper/2203.02027/) (2022)
- [Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings](https://mlsanity.com/paper/1709.02911/) (2017)

## Built on by (most cited first, 2 of 2)

- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI](https://mlsanity.com/paper/2210.02656/) (2022)

## Abstract (arXiv)

Accurate assessment of the domain expertise of developers is important for assigning the proper candidate to contribute to a project or to attend a job role. Since the potential candidate can come from a large pool, the automated assessment of this domain expertise is a desirable goal. While previous methods have had some success within a single software project, the assessment of a developer's domain expertise from contributions across multiple projects is more challenging. In this paper, we employ doc2vec to represent the domain expertise of developers as embedding vectors. These vectors are derived from different sources that contain evidence of developers' expertise, such as the description of repositories that they contributed, their issue resolving history, and API calls in their commits. We name it dev2vec and demonstrate its effectiveness in representing the technical specialization of developers. Our results indicate that encoding the expertise of developers in an embedding vector outperforms state-of-the-art methods and improves the F1-score up to 21%. Moreover, our findings suggest that ``issue resolving history'' of developers is the most informative source of information to represent the domain expertise of developers in embedding spaces.