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Erschienen in: Empirical Software Engineering 2/2022

Open Access 01.03.2022 | Author Correction

Correction to: Uniform and scalable sampling of highly configurable systems

verfasst von: Ruben Heradio, David Fernandez-Amoros, José A. Galindo, David Benavides, Don Batory

Erschienen in: Empirical Software Engineering | Ausgabe 2/2022

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The online version of the original article can be found at https://​doi.​org/​10.​1007/​s10664-021-10102-5

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Correction to: Empir Software Eng (2022) 27: 44
The original version of this article, unfortunately, contained mistakes. The title of Section 2.2.3 should be "Method 3: Measure the distance between the theoretical variable probabilities with the empirical variable frequencies in a sample", and the title of Section 2.2.4 should be "Method 4: A statistical goodness-of-fit test that compares the theoretical variable probabilities with the empirical variable frequencies in a sample".
The original article has been corrected.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

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Titel
Correction to: Uniform and scalable sampling of highly configurable systems
verfasst von
Ruben Heradio
David Fernandez-Amoros
José A. Galindo
David Benavides
Don Batory
Publikationsdatum
01.03.2022
Verlag
Springer US
Erschienen in
Empirical Software Engineering / Ausgabe 2/2022
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-022-10141-6

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