Unit Tests of Scientific Software: A Study on SWMM

Testing helps assure software quality by executing program and uncovering bugs. Scientific software developers often find it challenging to carry out systematic and automated testing due to reasons like inherent model uncertainties and complex floating point computations. We report in this paper a m...

Descripción completa

Detalles Bibliográficos
Autores principales: Peng, Zedong, Lin, Xuanyi, Niu, Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304759/
http://dx.doi.org/10.1007/978-3-030-50436-6_30
_version_ 1783548321264041984
author Peng, Zedong
Lin, Xuanyi
Niu, Nan
author_facet Peng, Zedong
Lin, Xuanyi
Niu, Nan
author_sort Peng, Zedong
collection PubMed
description Testing helps assure software quality by executing program and uncovering bugs. Scientific software developers often find it challenging to carry out systematic and automated testing due to reasons like inherent model uncertainties and complex floating point computations. We report in this paper a manual analysis of the unit tests written by the developers of the Storm Water Management Model (SWMM). The results show that the 1,458 SWMM tests have a 54.0% code coverage and a 82.4% user manual coverage. We also observe a “getter-setter-getter” testing pattern from the SWMM unit tests. Based on these results, we offer insights to improve test development and coverage.
format Online
Article
Text
id pubmed-7304759
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-73047592020-06-22 Unit Tests of Scientific Software: A Study on SWMM Peng, Zedong Lin, Xuanyi Niu, Nan Computational Science – ICCS 2020 Article Testing helps assure software quality by executing program and uncovering bugs. Scientific software developers often find it challenging to carry out systematic and automated testing due to reasons like inherent model uncertainties and complex floating point computations. We report in this paper a manual analysis of the unit tests written by the developers of the Storm Water Management Model (SWMM). The results show that the 1,458 SWMM tests have a 54.0% code coverage and a 82.4% user manual coverage. We also observe a “getter-setter-getter” testing pattern from the SWMM unit tests. Based on these results, we offer insights to improve test development and coverage. 2020-05-25 /pmc/articles/PMC7304759/ http://dx.doi.org/10.1007/978-3-030-50436-6_30 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
Peng, Zedong
Lin, Xuanyi
Niu, Nan
Unit Tests of Scientific Software: A Study on SWMM
title Unit Tests of Scientific Software: A Study on SWMM
title_full Unit Tests of Scientific Software: A Study on SWMM
title_fullStr Unit Tests of Scientific Software: A Study on SWMM
title_full_unstemmed Unit Tests of Scientific Software: A Study on SWMM
title_short Unit Tests of Scientific Software: A Study on SWMM
title_sort unit tests of scientific software: a study on swmm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304759/
http://dx.doi.org/10.1007/978-3-030-50436-6_30
work_keys_str_mv AT pengzedong unittestsofscientificsoftwareastudyonswmm
AT linxuanyi unittestsofscientificsoftwareastudyonswmm
AT niunan unittestsofscientificsoftwareastudyonswmm

Ejemplares similares