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Authors: Quentin Perez ; Christelle Urtado and Sylvain Vauttier

Affiliation: EuroMov Digital Health in Motion, Univ. Montpellier, IMT Mines Ales, Ales, France

Keyword(s): Software Engineering, Artificial Intelligence, Developer Classification, Empirical Software Engineering.

Abstract: Experienced developers are key for the success of software development projects. In open-source software development, due to openness and distance, one cannot always rely on interpersonal interactions to know who these key people are. Automating the mining of experienced developers is not an easy task either, because of the subjectivity and relativity of what experience is and also because the material to search from (code and development-related metadata) does not obviously relate developers to their capabilities. Some research works propose developer profiling or clustering solutions though, from which we take inspiration. This paper advocates that it is possible to learn from tangible metrics extracted from code and development-related artifacts who are the experienced developers. It uses a supervised learning-based approach trained with a manually labeled dataset of 703 developers from 17 open-source projects from GitHub for which 23 metrics are automatically extracted. Experienc ed developers classification results show a high F1 measure. A companion explainability study analyzes which metrics are the most influential. (More)

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Paper citation in several formats:
Perez, Q.; Urtado, C. and Vauttier, S. (2022). Mining Experienced Developers in Open-source Projects. In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-568-5; ISSN 2184-4895, SciTePress, pages 443-452. DOI: 10.5220/0011071800003176

@conference{enase22,
author={Quentin Perez. and Christelle Urtado. and Sylvain Vauttier.},
title={Mining Experienced Developers in Open-source Projects},
booktitle={Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2022},
pages={443-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011071800003176},
isbn={978-989-758-568-5},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Mining Experienced Developers in Open-source Projects
SN - 978-989-758-568-5
IS - 2184-4895
AU - Perez, Q.
AU - Urtado, C.
AU - Vauttier, S.
PY - 2022
SP - 443
EP - 452
DO - 10.5220/0011071800003176
PB - SciTePress

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