A mixed graph-relational dataset of socio-technical interactions in open source systems

Ashraf, Usman; Mayr-Dorn, Christoph; Egyed, Alexander; Panichella, Sebastiano

doi:10.1145/3379597.3387492

Publication type:	Conference paper
Type of review:	Not specified
Title:	A mixed graph-relational dataset of socio-technical interactions in open source systems
Authors:	Ashraf, Usman Mayr-Dorn, Christoph Egyed, Alexander Panichella, Sebastiano
et. al:	No
DOI:	10.1145/3379597.3387492
Proceedings:	Proceedings of the 17th International Conference on Mining Software Repositories
Page(s):	538
Pages to:	542
Conference details:	MSR '20: 17th International Conference on Mining Software Repositories, Seoul, South Korea, June 2020
Issue Date:	2020
Publisher / Ed. Institution:	Association for Computing Machinery
ISBN:	9781450375177
Language:	English
Subject (DDC):	005: Computer programming, programs and data
Abstract:	Several researchers have studied that developers contributing to open source systems tend to self-organize in "emerging" teams. The structure of these latent teams has a significant impact on software quality, with development teams structure somewhat reflected in the way developers communicate and contribute in the subsystems of a system. Therefore, in order to study socio-technical interactions as well as the software evolution dynamics of open source systems, in this paper, we present a novel dataset, gathered from 20 open source projects, which report the developers' activities in the scope of commits and issues at the level of subsystems. Thus, the new, generated dataset comprises of emerging and explicit links among developers, commits, issues, and source code artifacts, with data grouped around the subsystems point of view, which can be used to better study the system dynamics behind the extracted sociotechnical interactions.
URI:	https://resolver.obvsg.at/urn:nbn:at:at-ubl:3-5029 https://digitalcollection.zhaw.ch/handle/11475/20886
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Appears in collections:	Publikationen School of Engineering

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Ashraf, U., Mayr-Dorn, C., Egyed, A., & Panichella, S. (2020). A mixed graph-relational dataset of socio-technical interactions in open source systems [Conference paper]. Proceedings of the 17th International Conference on Mining Software Repositories, 538–542. https://doi.org/10.1145/3379597.3387492

Ashraf, U. et al. (2020) ‘A mixed graph-relational dataset of socio-technical interactions in open source systems’, in Proceedings of the 17th International Conference on Mining Software Repositories. Association for Computing Machinery, pp. 538–542. Available at: https://doi.org/10.1145/3379597.3387492.

U. Ashraf, C. Mayr-Dorn, A. Egyed, and S. Panichella, “A mixed graph-relational dataset of socio-technical interactions in open source systems,” in Proceedings of the 17th International Conference on Mining Software Repositories, 2020, pp. 538–542. doi: 10.1145/3379597.3387492.

ASHRAF, Usman, Christoph MAYR-DORN, Alexander EGYED und Sebastiano PANICHELLA, 2020. A mixed graph-relational dataset of socio-technical interactions in open source systems. In: Proceedings of the 17th International Conference on Mining Software Repositories [online]. Conference paper. Association for Computing Machinery. 2020. S. 538–542. ISBN 9781450375177. Verfügbar unter: https://resolver.obvsg.at/urn:nbn:at:at-ubl:3-5029

Ashraf, Usman, Christoph Mayr-Dorn, Alexander Egyed, and Sebastiano Panichella. 2020. “A Mixed Graph-Relational Dataset of Socio-Technical Interactions in Open Source Systems.” Conference paper. In Proceedings of the 17th International Conference on Mining Software Repositories, 538–42. Association for Computing Machinery. https://doi.org/10.1145/3379597.3387492.

Ashraf, Usman, et al. “A Mixed Graph-Relational Dataset of Socio-Technical Interactions in Open Source Systems.” Proceedings of the 17th International Conference on Mining Software Repositories, Association for Computing Machinery, 2020, pp. 538–42, https://doi.org/10.1145/3379597.3387492.