Literature DB >> 34017363

Unit and regression tests of scientific software: A study on SWMM.

Zedong Peng1, Xuanyi Lin1, Michelle Simon2, Nan Niu1.   

Abstract

Testing helps assure software quality by executing a 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. Extending the recent work on analyzing the unit tests written by the developers of the Storm Water Management Model (SWMM) [32], we report in this paper the investigation of both unit and regression tests of SWMM. The results show that the 2953 unit tests of SWMM have a 39.7% statement-level code coverage and a 82.4% user manual coverage. Meanwhile, an examination of 58 regression tests of SWMM shows a 44.9% statement-level code coverage and a near 100% user manual coverage. We also observe a "getter-setter-getter" testing pattern from the SWMM unit tests, and suggest a diversified way of executing regression tests.

Entities:  

Keywords:  Regression testing; Scientific software; Storm Water Management Model (SWMM); Test coverage; Unit testing; User manual

Year:  2021        PMID: 34017363      PMCID: PMC8128694          DOI: 10.1016/j.jocs.2021.101347

Source DB:  PubMed          Journal:  J Comput Sci


  5 in total

1.  Modelling of green roofs' hydrologic performance using EPA's SWMM.

Authors:  E Burszta-Adamiak; M Mrowiec
Journal:  Water Sci Technol       Date:  2013       Impact factor: 1.915

2.  Chaste: using agile programming techniques to develop computational biology software.

Authors:  Joe Pitt-Francis; Miguel O Bernabeu; Jonathan Cooper; Alan Garny; Lee Momtahan; James Osborne; Pras Pathmanathan; Blanca Rodriguez; Jonathan P Whiteley; David J Gavaghan
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2008-09-13       Impact factor: 4.226

3.  A Clustering-Based Approach to Enriching Code Foraging Environment.

Authors:  Nan Niu; Xiaoyu Jin; Zhendong Niu; Jing-Ru C Cheng; Ling Li; Mikhail Yu Kataev
Journal:  IEEE Trans Cybern       Date:  2015-04-22       Impact factor: 11.448

4.  Testing Scientific Software: A Systematic Literature Review.

Authors:  Upulee Kanewala; James M Bieman
Journal:  Inf Softw Technol       Date:  2014-10-01       Impact factor: 2.730

5.  Exploratory Metamorphic Testing for Scientific Software.

Authors:  Xuanyi Lin; Michelle Simon; Nan Niu
Journal:  Comput Sci Eng       Date:  2018-11-13       Impact factor: 2.080

  5 in total

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