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Authors: Inna Kurnosova 1 ; Dmitrii Timofeev 2 and Alexander Samochadin 2

Affiliations: 1 NeuroTech Lab, Institute of Applied Mathematics and Mechanics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg and Russia ; 2 Mobile Device Management Lab, Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia, Higher School of Software Engineering, Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, St. Petersburg and Russia

Keyword(s): Repository Mining, User Activity, User Modeling, Software Engineering.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Process Management ; e-Business ; Enterprise Engineering ; Enterprise Information Systems ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Symbolic Systems

Abstract: The paper studies the roles users play when contributing to open-source projects using modern code hosting and issue tracking platforms like GITHUB. Role identification has been performed using cluster analysis of the feature vectors generated from the events corresponding to user activity. The method was applied to three open-source projects of different sizes. The roles of maintainers and developers (core team), casual contributors, and watchers were identified, as well as the differences in work organization in these projects.

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Paper citation in several formats:
Kurnosova, I.; Timofeev, D. and Samochadin, A. (2019). Identification of User Activity Types using Issue Tracker Events. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KMIS; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 405-411. DOI: 10.5220/0008383604050411

@conference{kmis19,
author={Inna Kurnosova. and Dmitrii Timofeev. and Alexander Samochadin.},
title={Identification of User Activity Types using Issue Tracker Events},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KMIS},
year={2019},
pages={405-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008383604050411},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KMIS
TI - Identification of User Activity Types using Issue Tracker Events
SN - 978-989-758-382-7
IS - 2184-3228
AU - Kurnosova, I.
AU - Timofeev, D.
AU - Samochadin, A.
PY - 2019
SP - 405
EP - 411
DO - 10.5220/0008383604050411
PB - SciTePress

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