Unmasking Data Secrets: An Empirical Investigation into Data Smells and Their Impact on Data Quality-Reference-Cited by-同舟云学术

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Unmasking Data Secrets: An Empirical Investigation into Data Smells and Their Impact on Data Quality

Published:2024-04-14 Issue: Volume:1 Page:53-63
ISSN:
Container-title:Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI
language:
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Author:

Recupito Gilberto¹^ORCID,Rapacciuolo Raimondo¹^ORCID,Di Nucci Dario¹^ORCID,Palomba Fabio¹^ORCID

Affiliation:

1. University of Salerno, Fisciano, Italy

Funder

Ministero dell'Università e della Ricerca

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3644815.3644960

Reference54 articles.

1. Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study

2. AI Engineering: Realizing the Potential of AI

3. Data quality in empirical software engineering

4. Eric Breck Neoklis Polyzotis Sudip Roy Steven Whang and Martin Zinkevich. 2019. Data Validation for Machine Learning.. In MLSys.

5. On the application of measurement theory in software engineering

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