University of Oulu

Maëlick Claes, Mika Mäntylä, Miikka Kuutila, and Umar Farooq. 2018. Towards automatically identifying paid open source developers. In Proceedings of the 15th International Conference on Mining Software Repositories (MSR '18). ACM, New York, NY, USA, 437-441. DOI: https://doi.org/10.1145/3196398.3196447

Towards automatically identifying paid open source developers

Saved in:
Author: Claes, Maëlick1; Mäntylä, Mika1; Kuutila, Miikka1;
Organizations: 1M3S, ITEE, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201901031256
Language: English
Published: Association for Computing Machinery, 2018
Publish Date: 2019-01-03
Description:

Abstract

Open source development contains contributions from both hired and volunteer software developers. Identification of this status is important when we consider the transferability of research results to the closed source software industry, as they include no volunteer developers. While many studies have taken the employment status of developers into account, this information is often gathered manually due to the lack of accurate automatic methods. In this paper, we present an initial step towards predicting paid and unpaid open source development using machine learning and compare our results with automatic techniques used in prior work. By relying on code source repository meta-data from Mozilla, and manually collected employment status, we built a dataset of the most active developers, both volunteer and hired by Mozilla. We define a set of metrics based on developers' usual commit time pattern and use different classification methods (logistic regression, classification tree, and random forest). The results show that our proposed method identify paid and unpaid commits with an AUC of 0.75 using random forest, which is higher than the AUC of 0.64 obtained with the best of the previously used automatic methods.

see all

ISBN: 978-1-4503-5716-6
Pages: 437 - 441
DOI: 10.1145/3196398.3196447
OADOI: https://oadoi.org/10.1145/3196398.3196447
Host publication: MSR '18 Proceedings of the 15th International Conference on Mining Software Repositories
Conference: International Conference on Mining Software Repositories
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: The authors have been supported by Academy of Finland grant 298020.
Academy of Finland Grant Number: 298020
Detailed Information: 298020 (Academy of Finland Funding decision)
Copyright information: © ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in In Proceedings of the 15th International Conference on Mining Software Repositories (MSR '18). DOI: https://doi.org/10.1145/3196398.3196447