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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王凡(Farn Wang) | |
| dc.contributor.author | Chun-Hsien Yu | en |
| dc.contributor.author | 余俊賢 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:45:28Z | - |
| dc.date.available | 2023-02-23 | |
| dc.date.copyright | 2018-02-23 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-01-31 | |
| dc.identifier.citation | [1] MacarioPolo & PedroReals & MarioPiattini. Computing Test Automation; IEEE Software, VOL. 30, NO. 1, January 2013.
[2] IEEE Standard for Software and System Test Documentation, Software & Systems Engineering Standards Committee, 18 July 2008. [3] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large- Scale Hierarchical Image Database. In CVPR09, 2009. [4] Jin Yu, Boualem Benatallah, Regis Saint-Paul, Fabio Casati, Florian Daniel, and Maristella Matera. 2007. A framework for rapid integration of presentation components. In Proceedings of the 16th international conference on World Wide Web (WWW '07). ACM, New York, NY, USA, 923-932. [5] https://creativecommons.org/ [6] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541–551, Dec. 1989. 2, 4 [7] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by backpropagating errors. Nature, 1986. 2, 4 [8] Diederik P. Kingma, Jimmy Ba. Adam: A Method for Stochastic Optimization. In ICLR 2015. [9] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. CoRR, abs/1409.4842, 2014. 2, 3, 4, 5, 6, 10 [10] https://github.com/pwdyson/inflect.py [11] https://www.abisource.com/projects/enchant/ [12] http://www.nltk.org/ [13] BRUNELLE, Justin F.; WEIGLE, Michele C.; NELSON, Michael L. Combining Heritrix and PhantomJS for Better Crawling of Pages with JavaScript. 2016. [14] Jose Teixeira and Tingting Lin. 2014. Collaboration in the open-source arena: the webkit case. In Proceedings of the 52nd ACM conference on Computers and people research (SIGSIM-CPR '14). ACM, New York, NY, USA, 121-129. [15] S. R. Choudhary, H. Versee, A. Orso, 'WEBDIFF: Automated identification of cross-browser issues in web applications', IEEE ICSM, pp. 1-10, 2010. [16] NYMAN, Noel. Using monkey test tools. oftware Testing & Quality Engineering Magazine, 2000, 18-21. [17] P.M. Duernberger, 'Software testing applications in a computer science curriculum', IEEE Technical Applications Conference Northcon/96, pp. 291-293, 4–6 Nov 1996. [18] Tom Yeh, Tsung-Hsiang Chang, and Robert C. Miller. 2009. Sikuli: using GUI screenshots for search and automation. In Proceedings of the 22nd annual ACM symposium on User interface software and technology (UIST '09). ACM, New York, NY, USA, 183-192. [19] Zhaomeng Peng, Nengqiang He, Chunxiao Jiang, Zhihua Li, Lei Xu, Yipeng Li, and Yong Ren. 2012. Graph-Based AJAX Crawl: Mining Data from Rich Internet Applications. In Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering - Volume 03 (ICCSEE '12), Vol. 3. IEEE Computer Society, Washington, DC, USA, 590-594. [20] KAUR, Harpreet; GUPTA, Gagan. Comparative study of automated testing tools: Selenium, quick test professional and testcomplete. International Journal of Engineering Research and Applications, 2013, 3.5: 1739-43. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70136 | - |
| dc.description.abstract | 網頁及手機的普及使得在開發上面自動化的測試變得越發重要，快速和廣泛的測試活動一直在程式開發的流程中存在著。然而如何確保有效的分析與測試仍然需要許多人力及時間的投入，這一點上一直是一件十分耗時費力的事情。
在執行自動化測試的時候，會將測試分為兩種，一種是完全放任他自由地、隨機地讓程式在頁面上執行各種動作；另一種則是，規劃固定的運行模式讓一切行為模組化。得到的結果，前者是盡可能地做壓力測試得知系統或服務的極限或缺失；後者則是，測試已知的功能看是否健全完整。然而，兩者都有各自需要設定目標、對象及行為。對人類來說，辨識和分類是極其簡單的事情，可是對測試的過程來說卻是一件耗時費力的事。 在本文中，提出了一個針對網頁及手機頁面的視覺化檢視及測試方法，透過圖形分類的方法在整合及收集頁面資訊的同時，自動地分類給測試腳本做使用，並且提升測試的有效程度，不再只是盲目的隨機測試，進而做到快速、有效且有意義的測試功能。由於，目前並沒有相對應完整的資料庫，我們會事先在網站上搜集好目標的資料，然後對此作分析及學習。最後，用分類的方式將畫面的元素自動標示相對應的標籤，以加速測試的進行。 | zh_TW |
| dc.description.abstract | Nowadays, increasing number of developers are using software testing technology to ensure the quality of their products in both web and mobile applications. It is a technique that providing random event traces and observing to see if the system or applications crash or not. At the moment, analyzing testing traces is still a heavy time-consuming and labor-intensive work.
When processing software testing, we follow two testing protocols: one is to randomly execute actions on the page and the other is to set up modules of actions. For the result, the former one we can know that whether the service or system is able to overcome this kind of stress test and the later one is to test if our defined functions work well. However, both behaviors require setting targets and labeled them ahead. For human beings, it is such an easy job to classify or detect contents but for software testing, it is not. And this is the reason why we still require labor force for doing so. In this thesis, we present Smart-Eye, a visualized analysis service for both web pages and mobile applications. With the advantage of image classification, Smart-Eye helps software testers in organizing data and labeling them for the later testing scripts. Not only the analysis but the analysis improves the time consumption and accuracy. Because there is no complete dataset in this domain, we first gathered needed information and then built a classifying model from them. Last, we used it to do our intense work and made it an auto-labeling tool for later software testing in order to speed up the process of software testing in both aspects above. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:45:28Z (GMT). No. of bitstreams: 1 ntu-107-R04921119-1.pdf: 3487282 bytes, checksum: ccd3165f5c68030dfbba2a28f851e38d (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 .................................................... #
誌謝 ................................................................ 1 中文摘要 ............................................................ 2 ABSTRACT ............................................................ 3 CONTENTS ............................................................ 5 CHAPTER 1 INTRODUCTION ....................................................................................................... 7 1.1 Motivation ............................................................................................................... 9 1.2 Organization .......................................................................................................... 12 CHAPTER 2 BACKGROUND ....................................................................................................... 13 2.1 Page Design ........................................................................................................... 14 2.1.1 Static Content ............................................................................................................................... 14 2.1.2 Dynamic Content .......................................................................................................................... 15 2.2 Testing Tools........................................................................................................... 17 2.3 Crawler .................................................................................................................. 18 2.4 Image Classification ............................................................................................... 18 CHAPTER 3 RUNNING EXAMPLES .............................................................................................. 21 CHAPTER 4 RELATED WORK ..................................................................................................... 25 4.1 Automatic Software Testing .................................................................................. 25 4.1.1 Monkey Test ................................................................................................................................ 26 4.1.2 Gorilla Test ................................................................................................................................... 26 4.2 Sikuli ....................................................................................................................... 27 4.3 Crawljax ................................................................................................................. 28 CHAPTER 5 SMART-EYE .......................................................................................................... 29 5.1 Challenges in the Construction of Smart-Eye ......................................................... 30 5.2 Foundation of Smart-Eye ....................................................................................... 32 5.3 Classification Structure of Smart-Eye ..................................................................... 34 5.3.1 Training Mode of Smart-Eye ......................................................................................................... 34 5.3.2 Predicting Mode of Smart-Eye ...................................................................................................... 36 5.3.3 Updating Mode of Smart-Eye ....................................................................................................... 37 CHAPTER 6 DATASET AND EVALUATION ...................................................................................... 39 6.1 Data Arrangement ................................................................................................. 40 6.2 Refinement ............................................................................................................ 45 6.3 Final Adjustment .................................................................................................... 48 CHAPTER 7 EXPERIMENTS........................................................................................................ 51 7.1 Experiment Data Analysis ...................................................................................... 51 7.2 Experiment Platform .............................................................................................. 52 7.3 Research Question ................................................................................................. 53 7.3.1 RQ1: What is the appropriate size of dataset? ............................................................................. 53 7.3.2 RQ2: How accurate is Smart-Eye? ................................................................................................. 54 7.3.3 RQ3: Is there any help from traditional computer vision methods?............................................. 55 CHAPTER 8 CONCLUSION AND FUTURE WORK ............................................................................ 56 APPENDIX ........................................................... 57 REFERENCE .......................................................... 59 | |
| dc.language.iso | en | |
| dc.subject | 測試評估 | zh_TW |
| dc.subject | 腳本測試 | zh_TW |
| dc.subject | 自動化測試 | zh_TW |
| dc.subject | 自動分析 | zh_TW |
| dc.subject | Automatic Software Testing | en |
| dc.subject | Testing Analysis | en |
| dc.subject | Script Traces | en |
| dc.subject | Trace Evaluation | en |
| dc.title | 運用圖形分類在檢測網頁程式之分析系統 | zh_TW |
| dc.title | Using Image Classification for Automatic Page Analysis on the Testing of Web Apps | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳銘憲(Ming-Syan Chen),雷欽隆(Chin-Laung Lei),李宏毅(Hung-yi Lee) | |
| dc.subject.keyword | 自動化測試,自動分析,腳本測試,測試評估, | zh_TW |
| dc.subject.keyword | Automatic Software Testing,Testing Analysis,Script Traces,Trace Evaluation, | en |
| dc.relation.page | 60 | |
| dc.identifier.doi | 10.6342/NTU201800240 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-01-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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|---|---|---|---|
| ntu-107-1.pdf 未授權公開取用 | 3.41 MB | Adobe PDF |
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