Data Quality Assessment in the Wild: Findings from GitHub-Reference-Cited by-同舟云学术

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Data Quality Assessment in the Wild: Findings from GitHub

Published:2024-06-18 Issue: Volume: Page:120-129
ISSN:
Container-title:Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering
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Author:

Ustunboyacioglu Ipek¹^ORCID,Kumara Indika¹^ORCID,Di Nucci Dario²^ORCID,Tamburri Damian Andrew³^ORCID,Van Den Heuvel Willem-Jan¹^ORCID

Affiliation:

1. Jheronimus Academy of Data Science, Tilburg University, Netherlands

2. Department of Computer Science, University of Salerno, Italy

3. Jheronimus Academy of Data Science, Eindhoven University of Technology, Netherlands

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3661167.3661213

Reference23 articles.

1. Detecting data errors

2. Marcel Altendeitering and Tobias Moritz Guggenberger. 2021. Designing Data Quality Tools: Findings from an Action Design Research Project at Boehringer Ingelheim.. In ECIS.

3. José Barateiro and Helena Galhardas. 2005. A survey of data quality tools.Datenbank-Spektrum 14, 15-21 (2005), 48.

4. Carlo Batini, Cinzia Cappiello, Chiara Francalanci, and Andrea Maurino. 2009. Methodologies for data quality assessment and improvement. ACM Comput. Surv. 41, 3, Article 16 (jul 2009), 52 pages.

5. Hudson Borges, Andre Hora, and Marco Tulio Valente. 2016. Understanding the Factors That Impact the Popularity of GitHub Repositories. In 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME). 334–344.

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