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.
Author: | Mitchell Joblin, Wolfgang MauererORCiDGND, Sven Apel, Janet Siegmund, Dirk Riehle |
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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 |