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    題名: 動態符號執行測試用於自動深度網絡測試
    Dynamic Concolication for Automatic Deep Network Testing
    作者: 蔣其叡
    Chiang, Chi-Rui
    貢獻者: 郁方
    Yu, Fang
    蔣其叡
    Chiang, Chi-Rui
    關鍵詞: 動態符號執行測試
    自動單元測試
    Automatic Unit Testing
    Concolic Testing
    Python
    Dynamic Concolication
    NCCU
    日期: 2024
    上傳時間: 2024-09-04 14:04:08 (UTC+8)
    摘要: 結合具體測試和符號執行的動態符號執行測試(Concolic testing)已被證明在識別軟體漏洞方面非常有效。本文重點介紹如何應用 PyCT，一種動態符號執行測試工具，用於自動生成單元測試及其所需的輸入。我們的目標不僅是對目標程式進行動態符號執行測試，還通過使用動態子程序追蹤（DST）對目標程式呼叫的子程序和外部庫進行封裝，以實現符號執行，從而檢查其互動中的潛在漏洞。採用該方法的動機在於解決測試過程中動態符號執行變量過早降級的問題，例如，由於不支持的操作，這可能會妨礙後續測試中符號表達式的使用。通過將當前執行及其子程序的輸入升級為動態符號執行變量，我們可以減輕過早降級的影響，從而確保更全面的動態符號執行測試覆蓋率。在遇到無法升級為動態符號執行變量的輸入類型時，我們還在 DST 中引入了模糊測試技術。實驗結果證明了我們的方法在增強對各種 Python 庫的動態符號執行測試方面的有效性，展示了測試覆蓋率的提高和潛在漏洞的檢測能力。我們的方法能夠從最小的初始努力生成大量針對目標庫的測試用例。
    Concolic testing, which combines concrete testing and symbolic execution, has proven highly effective in identifying software vulnerabilities. This paper focuses on applying PyCT, a concolic testing tool, for the automated generation of unittests and their required inputs. Our objective is not only to perform concolic testing on the target program but also to employ Dynamic Subroutine Tracking (DST) to wrap the subroutines and external libraries called by the target program for symbolic execution, thereby checking for potential vulnerabilities in their interactions.
    The motivation behind this approach is to address the issue of premature downgrading of concolic variables during testing, e.g., due to unsupported operations, which can hinder subsequent testing from using symbolic expressions.
    By upgrading the inputs of current execution and its subroutines to concolic variables, we mitigate the impact of premature downgrading, thus ensuring a more comprehensive concolic testing coverage.
    We also incorporate fuzzing techniques in DST when encountering input types that cannot be upgraded to concolic variables.
    Experimental results demonstrate the effectiveness of our approach in enhancing concolic testing for various Python libraries, showcasing improved testing coverage and the detection of potential vulnerabilities. Our method can generate extensive test cases for target libraries from minimal initial efforts.
    參考文獻: Ahmadilivani, M. H., Taheri, M., Raik, J., Daneshtalab, M., and Jenihhin, M. (2023). A
    systematic literature review on hardware reliability assessment methods for deep neural
    networks.
    Araki., L. Y. and Peres., L. M. (2018). A systematic review of concolic testing with aplication
    of test criteria. In Proceedings of the 20th International Conference on Enterprise
    Information Systems - Volume 2: ICEIS, pages 121–132. INSTICC, SciTePress.
    Bai, T., Huang, S., Huang, Y., Wang, X., Xia, C., Qu, Y., and Yang, Z. (2024). Criticalfuzz:
    A critical neuron coverage-guided fuzz testing framework for deep neural networks.
    Information and Software Technology, 172:107476.
    Ball, T. and Daniel, J. (2015). Deconstructing dynamic symbolic execution. In Irlbeck,
    M., Peled, D. A., and Pretschner, A., editors, Dependable Software Systems Engineering,
    volume 40 of NATO Science for Peace and Security Series, D: Information and
    Communication Security, pages 26–41. IOS Press.
    Cadar, C. and Sen, K. (2013). Symbolic execution for software testing: three decades
    later. Commun. ACM, 56(2):82–90.
    Caniço, A. B. and Santos, A. L. (2023). Witter: A library for white-box testing of introductory
    programming algorithms. In Proceedings of the 2023 ACM SIGPLAN Interna-tional
    Symposium on SPLASH-E, SPLASH-E 2023, page 69–74, New York, NY, USA.
    Association for Computing Machinery.
    Chen, Y.-F., Tsai, W.-L., Wu, W.-C., Yen, D.-D., and Yu, F. (2021). Pyct: A python
    concolic tester. In Oh, H., editor, Programming Languages and Systems, pages 38–46,
    Cham. Springer International Publishing.
    Gopinath, D., Wang, K., Zhang, M., Pasareanu, C. S., and Khurshid, S. (2018). Symbolic
    execution for deep neural networks.
    Gu, J., Luo, X., Zhou, Y., and Wang, X. (2022). Muffin: Testing deep learning libraries
    via neural architecture fuzzing.
    Huang, J.-t., Zhang, J., Wang, W., He, P., Su, Y., and Lyu, M. R. (2022). Aeon: A method
    for automatic evaluation of nlp test cases. In Proceedings of the 31st ACM SIGSOFT
    International Symposium on Software Testing and Analysis, ISSTA 2022, page 202–
    214, New York, NY, USA. Association for Computing Machinery.
    Ji, P., Feng, Y., Liu, J., Zhao, Z., and Chen, Z. (2022). Asrtest: Automated testing for deepneural-
    network-driven speech recognition systems. In Proceedings of the 31st ACM
    SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2022,
    page 189–201, New York, NY, USA. Association for Computing Machinery.
    Khan, M. (2011). Different approaches to black box testing technique for finding errors.
    International Journal of Software Engineering Applications, 2.
    Klees, G., Ruef, A., Cooper, B., Wei, S., and Hicks, M. (2018). Evaluating fuzz testing.
    Li, R., Yang, P., Huang, C.-C., Sun, Y., Xue, B., and Zhang, L. (2022). Towards practical
    robustness analysis for dnns based on pac-model learning. In Proceedings of the 44th
    International Conference on Software Engineering, ICSE ’22, page 2189–2201, New
    York, NY, USA. Association for Computing Machinery.
    Liu, Z., Feng, Y., and Chen, Z. (2021). Dialtest: Automated testing for recurrent-neuralnetwork-
    driven dialogue systems. In Proceedings of the 30th ACM SIGSOFT International
    Symposium on Software Testing and Analysis, ISSTA 2021, page 115–126, New
    York, NY, USA. Association for Computing Machinery.
    Manès, V. J., Han, H., Han, C., Cha, S. K., Egele, M., Schwartz, E. J., and Woo, M. (2021).
    The art, science, and engineering of fuzzing: A survey. IEEE Transactions on Software
    Engineering, 47(11):2312–2331.
    Sen, K., Marinov, D., and Agha, G. (2005). Cute: a concolic unit testing engine for c. In
    Proceedings of the 10th European Software Engineering Conference Held Jointly with
    13th ACM SIGSOFT International Symposium on Foundations of Software Engineering,
    ESEC/FSE-13, page 263–272, New York, NY, USA. Association for Computing
    Machinery.
    Wang, S., Shrestha, N., Subburaman, A. K., Wang, J., Wei, M., and Nagappan, N. (2021a).
    Automatic unit test generation for machine learning libraries: How far are we? In
    2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pages
    1548–1560.
    Wang, Z., You, H., Chen, J., Zhang, Y., Dong, X., and Zhang, W. (2021b). Prioritizing
    test inputs for deep neural networks via mutation analysis. In 2021 IEEE/ACM 43rd
    International Conference on Software Engineering (ICSE), pages 397–409.
    Xia, C. S., Dutta, S., Misailovic, S., Marinov, D., and Zhang, L. (2023). Balancing effectiveness
    and flakiness of non-deterministic machine learning tests. In 2023 IEEE/ACM
    45th International Conference on Software Engineering (ICSE), pages 1801–1813.
    Xie, D., Li, Y., Kim, M., Pham, H. V., Tan, L., Zhang, X., and Godfrey, M. W. (2022).
    Docter: Documentation-guided fuzzing for testing deep learning api functions. ISSTA
    2022, page 176–188, New York, NY, USA. Association for Computing Machinery.
    Yang, C., Deng, Y., Yao, J., Tu, Y., Li, H., and Zhang, L. (2023). Fuzzing automatic
    differentiation in deep-learning libraries.
    Yu, F., Chi, Y.-Y., and Chen, Y.-F. (2024a). Constraint-based adversarial example synthesis.
    Yu, F., Chi, Y.-Y., and Chen, Y.-F. (2024b). Constraint-based adversarial example synthesis.
    Zhang, J. and Li, J. (2020). Testing and verification of neural-network-based safety-critical
    control software: A systematic literature review. Information and Software Technology,
    123:106296.
    Zhang, X., Sun, N., Fang, C., Liu, J., Liu, J., Chai, D., Wang, J., and Chen, Z. (2021).
    Predoo: Precision testing of deep learning operators. In Proceedings of the 30th ACM
    SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2021,
    page 400–412, New York, NY, USA. Association for Computing Machinery.
    Zhao, X., Qu, H., Xu, J., Li, X., Lv, W., and Wang, G.-G. (2023). A systematic review of
    fuzzing. Soft Comput., 28(6):5493–5522.
    描述: 碩士
    國立政治大學
    資訊管理學系
    111356024
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111356024
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

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