Unit tests of scientific software: A study on swmm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2020 - 20th International Conference, Proceedings
EditorsValeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Peter M.A. Sloot, Peter M.A. Sloot, Peter M.A. Sloot, Jack J. Dongarra, Sérgio Brissos, João Teixeira
PublisherSpringer Science and Business Media Deutschland GmbH
Pages413-427
Number of pages15
ISBN (Print)9783030504359
DOIs
StatePublished - 2020
Event20th International Conference on Computational Science, ICCS 2020 - Amsterdam, Netherlands
Duration: Jun 3 2020Jun 5 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12143 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Computational Science, ICCS 2020
Country/TerritoryNetherlands
CityAmsterdam
Period06/3/2006/5/20

Keywords

  • Scientific software
  • Storm Water Management Model (SWMM)
  • Test coverage
  • Test oracle
  • Unit testing
  • User manual

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