Dynamic Symbolic Execution Tool for Python']Python Programs

被引:1
|
作者
Ding, Xuefeng [1 ]
Huang, Wanyu [2 ]
Liu, Ying [3 ]
Chen Wantao [3 ]
Ding Xuyang [3 ]
机构
[1] Sichuan Univ, Chengdu 610041, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Cyber Secur, Chengdu 611731, Sichuan, Peoples R China
[3] Power China Chengdu Engn Corp Ltd, Chengdu 610072, Sichuan, Peoples R China
关键词
Dynamic symbolic execution; Automated testing; !text type='Python']Python[!/text] program; Dynamic symbolic execution tool;
D O I
10.1109/ICITBS.2016.88
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Dynamic symbolic execution is an important automated testing technique. Firstly, we introduce the traditional symbolic execution and dynamic symbolic execution technology, and then review the research status of Python application testing with dynamic symbolic execution technology. Then we compare and analyze the dynamic symbolic execution tools with the existing Python programs and Architectures, performance, and supported data types. Finally, the development direction of this field prospects. It is helpful for researchers to understand the progress of dynamic symbolic execution technology in current Python program and lay a foundation for future research work.
引用
收藏
页码:212 / 217
页数:6
相关论文
共 50 条
  • [1] Dynamic Inference of Likely Symbolic Tensor Shapes in Python']Python Machine Learning Programs
    Sen, Koushik
    Zheng, Daniel
    2024 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE, ICSE-SEIP 2024, 2024, : 147 - 156
  • [2] Dynamic Slicing of Python']Python Programs
    Chen, Zhifei
    Chen, Lin
    Zhou, Yuming
    Xu, Zhaogui
    Chu, William C.
    Xu, Baowen
    2014 IEEE 38TH ANNUAL INTERNATIONAL COMPUTERS, SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), 2014, : 219 - 228
  • [3] Symbolic Python']Python
    Ari, Niyazi
    Mamatnazarova, Nurayim
    PROCEEDINGS OF THE 2014 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTER AND COMPUTATION (ICECCO'14), 2014,
  • [4] Dynamic provisioning and execution of HPC workflows using Python']Python
    Harris, Chris
    O'Leary, Patrick
    Grauer, Michael
    Chaudhary, Aashish
    Kotfila, Chris
    O'Bara, Robert
    PROCEEDINGS OF PYHPC2016: 6TH WORKSHOP ON PYTHON FOR HIGH-PERFORMANCE AND SCIENTIFIC COMPUTING, 2016, : 1 - 8
  • [5] PYRELOAD: Dynamic Updating of Python']Python Programs by Reloading
    Tang, Wei
    Zhang, Min
    2018 25TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2018), 2018, : 229 - 238
  • [6] SymPy: symbolic computing in Python']Python
    Meurer, Aaron
    Smith, Christopher P.
    Paprocki, Mateusz
    Certik, Ondrej
    Kirpichev, Sergey B.
    Rocklin, Matthew
    Kumar, AMiT
    Ivanov, Sergiu
    Moore, Jason K.
    Singh, Sartaj
    Rathnayake, Thilina
    Vig, Sean
    Granger, Brian E.
    Muller, Richard P.
    Bonazzi, Francesco
    Gupta, Harsh
    Vats, Shivam
    Johansson, Fredrik
    Pedregosa, Fabian
    Curry, Matthew J.
    Terrel, Andy R.
    Roucka, Stepan
    Saboo, Ashutosh
    Fernando, Isuru
    Kulal, Sumith
    Cimrman, Robert
    Scopatz, Anthony
    PEERJ COMPUTER SCIENCE, 2017,
  • [7] DrPython']Python-WEB: A Tool to Help Teaching Well-Written Python']Python Programs
    Battistini, Tommaso
    Isaia, Nicolo
    Sterbini, Andrea
    Temperini, Marco
    SOFTWARE ENGINEERING AND FORMAL METHODS: SEFM 2021 COLLOCATED WORKSHOPS, 2022, 13230 : 277 - 286
  • [9] Interactive Python']Python Programs for Crystallography
    Julian, M.
    Julian, F.
    Jones, H.
    ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, 2020, 76 : A66 - A66
  • [10] Measuring Polymorphism in Python']Python Programs
    Akerblom, Beatrice
    Wrigstad, Tobias
    ACM SIGPLAN NOTICES, 2016, 51 (02) : 114 - 128