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From Developer Networks to Verified Communities: A Fine-Grained Approach

  • Effective software engineering demands a coordinated effort. Unfortunately, a comprehensive view on developer coordination is rarely available to support software-engineering decisions, despite the significant implications on software quality, software architecture, and developer productivity. We present a fine-grained, verifiable, and fully automated approach to capture a view on developer coordination, based on commit information and source-code structure, mined from version-control systems. We apply methodology from network analysis and machine learning to identify developer communities automatically. Compared to previous work, our approach is fine-grained, and identifies statistically significant communities using order-statistics and a community-verification technique based on graph conductance. To demonstrate the scalability and generality of our approach, we analyze ten open-source projects with complex and active histories, written in various programming languages. By surveying 53 open-source developers from the ten projects, we validate the authenticity of inferred community structure with respect to reality. Our results indicate that developers of open-source projects form statistically significant community structures and this particular view on collaboration largely coincides with developers' perceptions of real-world collaboration.

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Metadaten
Author:Mitchell Joblin, Wolfgang MauererORCiDGND, Sven Apel, Janet Siegmund, Dirk Riehle
DOI:https://doi.org/10.1109/icse.2015.73
ISBN:978-1-4799-1934-5
Parent Title (English):2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, 16-24 May 2015, Florence, Italy
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2015
Release Date:2022/05/10
Tag:Computer languages; Measurement; Open Source Software; Software systems; Standards; collaboration
First Page:563
Last Page:573
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Labor für Digitalisierung (LFD)
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG