Coverage-Based Grammar-Guided Genetic Programming Generation of Test Suites

被引:1
|
作者
Ibias, Alfredo [1 ]
Vazquez-Gomis, Pablo [1 ]
Benito-Parejo, Miguel [1 ]
机构
[1] Univ Complutense Madrid, DTRS Res Grp, Madrid 28040, Spain
来源
2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) | 2021年
关键词
Genetic Programming; Coverage; Software Testing; SELECTION; SYSTEMS; TOOL;
D O I
10.1109/CEC45853.2021.9504969
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Software testing is fundamental to ensure the reliability of software. To properly test software, it is critical to generate test suites with high fault finding ability. We propose a new method to generate such test suites: a coverage-based grammar-guide genetic programming algorithm. This evolutionary computation based method allows us to generate test suites that conform with respect to a specification of the system under test using the coverage of such test suites as a guide. We considered scenarios for both black-box testing and white-box testing, depending on the different criteria we work with at each situation. Our experiments show that our proposed method outperforms other baseline methods, both in performance and execution time.
引用
收藏
页码:2411 / 2418
页数:8
相关论文
共 50 条
  • [1] On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming
    Sobania, Dominik
    GENETIC PROGRAMMING, EUROGP 2021, 2021, 12691 : 130 - 145
  • [2] Mining exceptional relationships with grammar-guided genetic programming
    Maria Luna, Jose
    Pechenizkiy, Mykola
    Ventura, Sebastian
    KNOWLEDGE AND INFORMATION SYSTEMS, 2016, 47 (03) : 571 - 594
  • [3] Mining exceptional relationships with grammar-guided genetic programming
    Jose Maria Luna
    Mykola Pechenizkiy
    Sebastian Ventura
    Knowledge and Information Systems, 2016, 47 : 571 - 594
  • [4] Automatic generation of algorithms for robust optimisation problems using Grammar-Guided Genetic Programming
    Hughes, Martin
    Goerigk, Marc
    Dokka, Trivikram
    COMPUTERS & OPERATIONS RESEARCH, 2021, 133
  • [5] Hierarchical Grammar-Guided Genetic Programming Techniques for Scheduling in Heterogeneous Networks
    Saber, Takfarinas
    Lynch, David
    Fagan, David
    Kucera, Stepan
    Claussen, Holger
    O'Neill, Michael
    2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2020,
  • [6] Extending Program Synthesis Grammars for Grammar-Guided Genetic Programming
    Forstenlechner, Stefan
    Fagan, David
    Nicolau, Miguel
    O'Neill, Michael
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XV, PT I, 2018, 11101 : 197 - 208
  • [7] Predicting Defects in Software Using Grammar-Guided Genetic Programming
    Tsakonas, Athanasios
    Dounias, Georgios
    ARTIFICIAL INTELLIGENCE: THEORIES, MODELS AND APPLICATIONS, SETN 2008, 2008, 5138 : 413 - 418
  • [8] Evolving Generalizable Multigrid-Based Helmholtz Preconditioners with Grammar-Guided Genetic Programming
    Schmitt, Jonas
    Koestler, Harald
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22), 2022, : 1009 - 1018
  • [9] Toward Evolving Dispatching Rules With Flow Control Operations by Grammar-Guided Linear Genetic Programming
    Huang, Zhixing
    Mei, Yi
    Zhang, Fangfang
    Zhang, Mengjie
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2025, 29 (01) : 217 - 231
  • [10] Comparing the expressive power of Strongly-Typed and Grammar-Guided Genetic Programming
    Fonseca, Alcides
    Pocas, Diogo
    PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023, 2023, : 1100 - 1108