Dev2vec: Representing Domain Expertise of Developers in an Embedding Space

TL;DR

This paper introduces dev2vec, a method that uses embedding vectors derived from various developer activity sources to accurately represent and assess developers' domain expertise across multiple projects.

Contribution

The paper presents dev2vec, a novel approach employing doc2vec to encode developers' expertise from diverse sources, outperforming existing methods in representing technical specialization.

Findings

Encoding developer expertise as embeddings improves F1-score by up to 21%.

Issue resolving history is the most informative source for expertise representation.

Dev2vec outperforms state-of-the-art methods in accuracy.

Abstract

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.