DEVS-SCRIPTING: A BLACK-BOX TEST FRAME FOR DEVS MODELS

被引:3
|
作者
McLaughlin, Matthew B. [1 ]
Sarjoughian, Hessam S. [1 ]
机构
[1] Arizona State Univ, Arizona Ctr Integrat Modeling & Simulat, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85281 USA
关键词
D O I
10.1109/WSC48552.2020.9384024
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Experimental frames have been used in DEVS-based simulations to drive scenarios through injecting inputs and interpreting outputs. This design has traditionally called for separate models with distinct roles: generator, acceptor, and transducer. In certain controlled experiments such as model testing, sequential programming offers a simpler design with many benefits, specifically: code reduction, test case development throughput, and diagnostics for failed tests. This research offers a test framework that is derived from atomic DEVS and facilitates testing through scripting. The challenge for this research is to prove DEVS semantics are maintained when the experimental frame is tightly controlled by a script. Our solution uses a separate thread for this script and synchronizes program execution switching with a nest lock. Synchronization is key in showing that this design maintains DEVS semantics by nesting script code within the state transition functions of DEVS modeling components.
引用
收藏
页码:2196 / 2207
页数:12
相关论文
共 50 条
  • [1] Black-Box Test Generation from Inferred Models
    Papadopoulos, Petros
    Walkinshaw, Neil
    2015 IEEE/ACM FOURTH INTERNATIONAL WORKSHOP ON REALIZING ARTIFICIAL INTELLIGENCE SYNERGIES IN SOFTWARE ENGINEERING (RAISE 2015), 2015, : 19 - 24
  • [2] White-Box and Black-Box Test Quality Metrics for Configurable Simulation Models
    Markiegi, Urtzi
    Arrieta, Aitor
    Etxeberria, Leire
    Sagardui, Goiuria
    23RD INTERNATIONAL SYSTEMS AND SOFTWARE PRODUCT LINE CONFERENCE(SPLC 2019), VOL B, 2019, : 211 - 214
  • [3] Interpretable Companions for Black-Box Models
    Pan, Danqing
    Wang, Tong
    Hara, Satoshi
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108, 2020, 108 : 2444 - 2453
  • [4] Causal Interpretations of Black-Box Models
    Zhao, Qingyuan
    Hastie, Trevor
    JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2021, 39 (01) : 272 - 281
  • [5] Evolving a Test Oracle in Black-Box Testing
    Wang, Farn
    Wu, Jung-Hsuan
    Huang, Chung-Hao
    Chang, Kai-Hsiang
    FUNDAMENTAL APPROACHES TO SOFTWARE ENGINEERING, 2011, 6603 : 310 - 325
  • [6] Comparing White-box and Black-box Test Prioritization
    Henard, Christopher
    Papadakis, Mike
    Harman, Mark
    Jia, Yue
    Le Traon, Yves
    2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), 2016, : 523 - 534
  • [7] Black-Box Test-Cost Reduction Based on Bayesian Network Models
    Pan, Renjian
    Zhang, Zhaobo
    Li, Xin
    Chakrabarty, Krishnendu
    Gu, Xinli
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2021, 40 (02) : 386 - 399
  • [8] OneMax in Black-Box Models with Several Restrictions
    Carola Doerr
    Johannes Lengler
    Algorithmica, 2017, 78 : 610 - 640
  • [9] ONEMAX in Black-Box Models with Several Restrictions
    Doerr, Carola
    Lengler, Johannes
    ALGORITHMICA, 2017, 78 (02) : 610 - 640
  • [10] Testing Framework for Black-box AI Models
    Aggarwal, Aniya
    Shaikh, Samiulla
    Hans, Sandeep
    Haldar, Swastik
    Ananthanarayanan, Rema
    Saha, Diptikalyan
    2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2021), 2021, : 81 - 84