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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/153152


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153152


    Title: 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
    Authors: 蔣其叡
    Chiang, Chi-Rui
    Contributors: 郁方
    Yu, Fang
    蔣其叡
    Chiang, Chi-Rui
    Keywords: 動態符號執行測試
    自動單元測試
    Automatic Unit Testing
    Concolic Testing
    Python
    Dynamic Concolication
    NCCU
    Date: 2024
    Issue Date: 2024-09-04 14:04:08 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    111356024
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111356024
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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