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Testing Research Software: A Case Study

Background: The increasing importance of software for the conduct of various types of research raises the necessity of proper testing to ensure correctness. The unique characteristics of the research software produce challenges in the testing process that require attention. Aims: Therefore, the goal...

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Autores principales: Eisty, Nasir U., Perez, Danny, Carver, Jeffrey C., Moulton, J. David, Nam, Hai Ah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304767/
http://dx.doi.org/10.1007/978-3-030-50436-6_33
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author Eisty, Nasir U.
Perez, Danny
Carver, Jeffrey C.
Moulton, J. David
Nam, Hai Ah
author_facet Eisty, Nasir U.
Perez, Danny
Carver, Jeffrey C.
Moulton, J. David
Nam, Hai Ah
author_sort Eisty, Nasir U.
collection PubMed
description Background: The increasing importance of software for the conduct of various types of research raises the necessity of proper testing to ensure correctness. The unique characteristics of the research software produce challenges in the testing process that require attention. Aims: Therefore, the goal of this paper is to share the experience of implementing a testing framework using a statistical approach for a specific type of research software, i.e. non-deterministic software. Method: Using the ParSplice research software project as a case, we implemented a testing framework based on a statistical testing approach called Multinomial Test. Results: Using the new framework, we were able to test the ParSplice project and demonstrate correctness in a situation where traditional methodical testing approaches were not feasible. Conclusions: This study opens up the possibilities of using statistical testing approaches for research software that can overcome some of the inherent challenges involved in testing non-deterministic research software.
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spelling pubmed-73047672020-06-22 Testing Research Software: A Case Study Eisty, Nasir U. Perez, Danny Carver, Jeffrey C. Moulton, J. David Nam, Hai Ah Computational Science – ICCS 2020 Article Background: The increasing importance of software for the conduct of various types of research raises the necessity of proper testing to ensure correctness. The unique characteristics of the research software produce challenges in the testing process that require attention. Aims: Therefore, the goal of this paper is to share the experience of implementing a testing framework using a statistical approach for a specific type of research software, i.e. non-deterministic software. Method: Using the ParSplice research software project as a case, we implemented a testing framework based on a statistical testing approach called Multinomial Test. Results: Using the new framework, we were able to test the ParSplice project and demonstrate correctness in a situation where traditional methodical testing approaches were not feasible. Conclusions: This study opens up the possibilities of using statistical testing approaches for research software that can overcome some of the inherent challenges involved in testing non-deterministic research software. 2020-05-25 /pmc/articles/PMC7304767/ http://dx.doi.org/10.1007/978-3-030-50436-6_33 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Eisty, Nasir U.
Perez, Danny
Carver, Jeffrey C.
Moulton, J. David
Nam, Hai Ah
Testing Research Software: A Case Study
title Testing Research Software: A Case Study
title_full Testing Research Software: A Case Study
title_fullStr Testing Research Software: A Case Study
title_full_unstemmed Testing Research Software: A Case Study
title_short Testing Research Software: A Case Study
title_sort testing research software: a case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304767/
http://dx.doi.org/10.1007/978-3-030-50436-6_33
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