Finding Metamorphic Relations for Scientific Software

被引:6
作者
Lin, Xuanyi [1 ]
Peng, Zedong [1 ]
Niu, Nan [1 ]
Wang, Wentao [2 ]
Liu, Hui [3 ]
机构
[1] Univ Cincinnati, Cincinnati, OH 45221 USA
[2] Oracle, Austin, TX USA
[3] Beijing Inst Technol, Beijing, Peoples R China
来源
2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2021) | 2021年
关键词
Scientific software; metamorphic relation identification; Storm Water Management Model (SWMM);
D O I
10.1109/ICSE-Companion52605.2021.00118
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Metamorphic testing uncovers defects by checking whether a relation holds among multiple software executions. These relations are known as metamorphic relations (MRs). For scientific software operating in a large multi-parameter input space, identifying MRs that determine the simultaneous changes among multiple variables is challenging. In this poster, we propose a fully automatic approach to classifying input and output variables from scientific software's user manual, mining these variables' associations from the user forum to generate MRs, and validating the MRs with existing regression tests. Preliminary results of our end-to-end MR support for the Storm Water Management Model (SWMM) are reported.
引用
收藏
页码:254 / 255
页数:2
相关论文
共 47 条
  • [41] Link-based approach to study scientific software usage: the case of VOSviewer
    Enrique Orduña-Malea
    Rodrigo Costas
    Scientometrics, 2021, 126 : 8153 - 8186
  • [42] Learning I/O Variables from Scientific Software's User Manuals
    Peng, Zedong
    Lin, Xuanyi
    Santhoshkumar, Sreelekhaa Nagamalli
    Niu, Nan
    Kanewala, Upulee
    COMPUTATIONAL SCIENCE, ICCS 2022, PT IV, 2022, : 503 - 516
  • [43] EESSI: A cross-platform ready-to-use optimised scientific software stack
    Droge, Bob
    Rusu, Victor Holanda
    Hoste, Kenneth
    van Leeuwen, Caspar
    O'Cais, Alan
    Roblitz, Thomas
    SOFTWARE-PRACTICE & EXPERIENCE, 2023, 53 (01) : 176 - 210
  • [44] PYTHIA-II: A knowledge/database system for managing performance data and recommending scientific software
    Houstis, EN
    Catlin, AC
    Rice, JR
    Verykios, VS
    Ramakrishnan, N
    Houstis, CE
    ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, 2000, 26 (02): : 227 - 253
  • [45] Investigating test selection techniques for scientific software using Hook's mutation sensitivity testing
    Gray, Rob
    Kelly, Diane
    ICCS 2010 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, PROCEEDINGS, 2010, 1 (01): : 1481 - 1488
  • [46] Guaiaca - a software tool for supporting the use of scientific applications designed for data analysis - part I: execution assistant
    Rodrigues Medeiros, Gil Carlos
    Barros, Willian Silva
    Laurino Dionello, Nelson Jose
    SEMINA-CIENCIAS AGRARIAS, 2016, 37 (04): : 2269 - 2279
  • [47] A Comparative Evaluation on the Quality of Manual and Automatic Test Case Generation Techniques for Scientific Software - A Case Study of a Python']Python Project for Material Science Workflows
    Truebenbach, Daniel
    Mueller, Sebastian
    Grunske, Lars
    15TH SEARCH-BASED SOFTWARE TESTING WORKSHOP (SBST 2022), 2022, : 6 - 13