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研究生： 劉權璋
Liu, Quan-Zhang
論文名稱： 圖形生成自動化測試程式碼之設計與實現
Design and Realization of Automatic Test Code for Graph Generation
指導教授： 賴槿峰
Lai, Chin-Feng
學位類別： 碩士
Master
系所名稱： 工學院 - 工程科學系碩士在職專班
Department of Engineering Science (on the job class)
論文出版年： 2022
畢業學年度： 110
語文別： 中文
論文頁數： 54
中文關鍵詞： 網頁自動化 、網頁測試 、程式碼生成
外文關鍵詞： Web Automation Testing, Web Test, Pix2code, Code generation
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    • 進入機器人時代，許多人工檢驗流程已開始使用自動化完成作業，過往需要倚靠大量人力土法煉鋼的檢驗品質，且需要耗費許多的時間與成本，所以近年來許多公司大量導入自動化測試，期望透過自動化測試協助人員，降低時間與人力成本，但是鑒於產品開發流程，測試是最後一個階段，故往往可以開發自動化測試的時間被嚴重壓縮，本論文提出圖形生成自動化測試程式碼之設計與實現，透過初期 UI/UX 設計的網頁介面圖即可以生成關鍵程式碼再經過執行六道程序後，即完成快速開發自動化測試程式碼，並可應用於產品開發流程各階段，將有效縮短自動化測試開發時程與提高產品品質。

    In the era of robots, many manual inspection processes have begun to use automation to complete operations. In the past, it was necessary to rely on a large amount of manual labor to inspect the quality of steelmaking, and it took a lot of time and cost. Therefore, in recent years, many companies have introduced many automated tests, hoping to use automation Testing assistance personnel to reduce time and labor costs. However, given that the product development process, testing is the last stage, the time that can be developed for automated testing is often severely compressed. This paper proposes the design and implementation of automated test code for the graph generation. The UI/UX design web interface diagram can generate the key code and execute the six procedures to complete the rapid development of the automated test code, which can be applied to all stages of the product development process, which will effectively shorten the automated test development timeline and improve it. product quality.

    摘要 I Extended Abstract II 內文目錄 VI 表目錄 VIII 圖目錄 IX 第一章 緒論 1 1.1 研究動機 1 1.2 研究方向與貢獻 5 1.3 章節提要 7 第二章 背景介紹與文獻探討 8 2.1系統發展生命周期 8 2.2自動化測試流程及自動化測試框架 10 2.3 網頁自動化工具研究 12 2.3.1 Selenium之研究與應用 12 2.3.2 AutoIT之研究與應用 16 2.3.3 測試網路速度之研究與應用 17 2.4 Pix2code之研究與應用 18 2.4.1 Pix2code -Vision Model 19 2.4.2 Pix2code -Language Model 20 2.4.3 Pix2code -Decoder 21 2.4.4 Pix2code -Training and Sampling 21 第三章 研究方法 22 3.1系統總體架構 22 3.2 系統功能 25 3.2.1圖形標記與轉譯程式碼 25 3.2.2 程式碼環境初始化 28 3.2.3 瀏覽器驅動自動檢查與下載 29 3.2.4 動態抓取載入時間與Retry次數 30 3.2.5 自動檢查程式碼完整性與自動修補 34 第四章 系統開發與測試環境及結果分析 37 4.1 系統開發與測試環境 37 4.1.1 系統開發環境 37 4.1.2 測試環境 39 4.2 結果分析 40 4.2.1網路速度與網頁載入時間穩定度的關係 40 4.2.2增加網頁載入時間的取樣數與比例值關係 42 4.2.3比對無檢查機制與有檢查機制下的成功率 44 4.2.4依照登入網頁設計的複雜度，測試本系統的成功率 45 第五章 結論與未來展望 49 5.1 結論 49 5.2 未來展望 51 參考文獻 52

    1. Boehm, B.W., Verifying and Validating Software Requirements and Design Specifications. IEEE Software, 1984. 1: p. 75-88.
    2. Product requirements document (2020,October 22). 維基百科, 自由的百科全書; Available from: https://en.wikipedia.org/wiki/Product_requirements_document.
    3. IEEE, IEEE Standard for Software and System Test Documentation. IEEE Std 829-2008, 2008.
    4. DinoSu. 測試案例設計入門. 2019; Available from: https://hackmd.io/@P8NJN5_XQQqQU4i2YIJzEg/HJmQYaknZ?type=view#%E6%B8%AC%E8%A9%A6%E6%A1%88%E4%BE%8B%E8%A8%AD%E8%A8%88%E5%85%A5%E9%96%80.
    5. Marick, B., When should a test be automated. Proceedings of The 11th International Software/Internet Quality Week, 1998: p. 1-20.
    6. Joel and Lalit, State-of-Testing-Report-2021. 2021.
    7. Document Object Model (2021,September 29). 維基百科, 自由的百科全書; Available from: https://en.wikipedia.org/wiki/Document_Object_Model.
    8. Silva, J. and D. Insa, Using the DOM Tree for Content Extraction. Electronic Proceedings in Theoretical Computer Science, 2012. 98.
    9. Beltramelli, T. pix2code: Generating Code from a Graphical User Interface Screenshot. 2017. arXiv:1705.07962.
    10. Royce, W.W., Managing the development of large software systems: concepts and techniques, in Proceedings of the 9th international conference on Software Engineering. 1987, IEEE Computer Society Press: Monterey, California, USA. p. 328–338.
    11. Pocatilu, P., Automated software testing process. Economy Informatics, 2002. 1: p. 97-99.
    12. Lim, J.-H., et al., An automated test method for robot platform and its components. International Journal of Software Engineering and Its Applications, 2010. 4(3): p. 9-18.
    13. Systems development life cycle (2021,November 17). 維基百科, 自由的百科全書; Available from: https://en.wikipedia.org/wiki/Systems_development_life_cycle.
    14. Boehm, B. and W.J. Hansen, Spiral development: Experience, principles, and refinements. 2000, CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INST.
    15. Dyba, T. and T. Dingsoyr, What Do We Know about Agile Software Development? IEEE Software, 2009. 26(5): p. 6-9.
    16. Martin, J., Rapid application development. 1991: Macmillan Publishing Co., Inc.
    17. Larman, C. and V.R. Basili, Iterative and incremental developments. a brief history. Computer, 2003. 36(6): p. 47-56.
    18. Alshamrani, A. and A. Bahattab, A comparison between three SDLC models waterfall model, spiral model, and Incremental/Iterative model. International Journal of Computer Science Issues (IJCSI), 2015. 12(1): p. 106.
    19. Baker, P., et al., Data-driven testing, in Model-Driven Testing. 2008, Springer. p. 87-95.
    20. Laukkanen, P., Data-driven and keyword-driven test automation frameworks. Master's thesis. Helsinki University of Technology, 2006.
    21. Utting, M. and B. Legeard, Practical model-based testing: a tools approach. 2010: Elsevier.
    22. Solis, C. and X. Wang. A Study of the Characteristics of Behaviour Driven Development. in 2011 37th EUROMICRO Conference on Software Engineering and Advanced Applications. 2011.
    23. Selenium. Available from: http://docs.seleniumhq.org.
    24. The Selenium Browser Automation Project (2021,November 23). Available from: https://www.selenium.dev/documentation/.
    25. Selenium Grid 4 (2021,November 23). Available from: https://www.selenium.dev/documentation/grid/.
    26. W3C standards define (2021). Available from: https://www.w3.org/standards/.
    27. Sheth, H. Selenium 4 Is Now W3C Compliant: All You Need To Know (2020,November 17). Available from: https://www.lambdatest.com/blog/selenium4-w3c-webdriver-protocol/.
    28. Selenium WebDriver Communication (2021m,November 23). Available from: https://www.selenium.dev/documentation/webdriver/understanding_the_components/.
    29. Selenium Browser manipulation (2021,November 23). Available from: https://www.selenium.dev/documentation/webdriver/browser_manipulation/.
    30. AutoIt. Available from: https://www.autoitscript.com/wiki/AutoIt_Introduction.
    31. Brand, J. and J. Balvanz, Automation is a breeze with AutoIt, in Proceedings of the 33rd annual ACM SIGUCCS conference on User services. 2005, Association for Computing Machinery: Monterey, CA, USA. p. 12–15.
    32. SPEEDTEST. Available from: https://www.speedtest.net/speedtest-servers.
    33. Internet Control Message Protocol (2021, Oct 08). 維基百科, 自由的百科全書; Available from: https://en.wikipedia.org/wiki/Internet_Control_Message_Protocol.
    34. Fowler, M., Domain-specific languages. 2010: Pearson Education.
    35. Albawi, S., T.A. Mohammed, and S. Al-Zawi. Understanding of a convolutional neural network. in 2017 International Conference on Engineering and Technology (ICET). 2017. Ieee.
    36. Fine, T.L., Feedforward neural network methodology. 2006: Springer Science & Business Media.
    37. Krizhevsky, A., I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25: p. 1097-1105.
    38. Sermanet, P., et al., Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013.
    39. Simonyan, K. and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
    40. Hochreiter, S. and J. Schmidhuber, Long short-term memory. Neural computation, 1997. 9(8): p. 1735-1780.
    41. Schuster, M. and K.K. Paliwal, Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 1997. 45(11): p. 2673-2681.
    42. Mikolov, T., et al. Distributed representations of words and phrases and their compositionality. in Advances in neural information processing systems. 2013.
    43. Graves, A., Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.
    44. Karpathy, A. and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
    45. mugekural. Web page screenshots and text contents from various websites (2019,November 11). Available from: https://www.kaggle.com/mugekural/web-pages.
    46. ChromeDriver. Available from: https://chromedriver.chromium.org/downloads.
    47. WebDriverWait. Available from: https://selenium-python.readthedocs.io/waits.html.
    48. NetBalancer Application. Available from: https://netbalancer.com/.
    49. GitHub. Available from: https://github.com/.
    50. 99个漂亮的注册和登录页设计(2013,September 06). Available from: https://www.uisdc.com/99-beautiful-free-signin-login-and-signup.

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