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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66798Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 王凡(Farn Wang) | |
| dc.contributor.author | Ta-Jung Cheng | en |
| dc.contributor.author | 鄭大容 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:08:33Z | - |
| dc.date.available | 2020-02-10 | |
| dc.date.copyright | 2020-02-10 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-01-31 | |
| dc.identifier.citation | T. D. Nguyen, A. T. Nguyen, H. D. Phan, and T. N. Nguyen, “Exploring API Embedding for API Usages and Applications,” in Proceedings of the 39th International Conference on Software Engineering, Buenos Aires, Argentina, 2017, pp. 438-449.
R. Gopalakrishnan, P. Sharma, M. Mirakhorli, and M. Galster, “Can Latent Topics in Source Code Predict Missing Architectural Tactics?” in Proceedings of the 39th International Conference on Software Engineering, Buenos Aires, Argentina, 2017, pp. 15-26. P. Liu, X. Zhang, M. Pistoia, Y. Zheng, M. Marques, and L. Zeng, “Automatic Text Input Generation for Mobile Testing,” in Proceedings of the 39th International Conference on Software Engineering, Buenos Aires, Argentina, 2017, pp. 643-653. C. Wang, F. Pastore, A. Goknil, L. Briand, and Z. Iqbal, “Automatic generation of system test cases from use case specifications,” in Proceedings of the 2015 International Symposium on Software Testing and Analysis, Baltimore, MD, USA, 2015, pp. 385-396. C. Aggarwal, “Collaborative crawling: mining user experiences for topical resource discovery,” in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, Edmonton, Alberta, Canada, 2002, pp. 423-428. G. Pant and P. Srinivasan, “Learning to crawl: Comparing classification schemes,” ACM Trans. Inf. Syst, vol. 23, iss. 4, pp. 430-462, Oct. 2005. K. Zhai, J. Boyd-Graber, N. Asadi, and M. L. Alkhouja, “Mr. LDA: a flexible large scale topic modeling package using variational inference in MapReduce,” in Proceedings of the 21st international conference on World Wide Web, Lyon, France, 2012, pp. 879-888. A. Rau, “Topic-driven testing,” in Proceedings of the 39th International Conference on Software Engineering Companion, Buenos Aires, Argentina, 2017, pp. 409-412. G. Wassermann, D. Yu, A. Chander, D. Dhurjati, H. Inamura, and Z. Su, “Dynamic Test Input Generation for Web Applications,” in Proceedings of the 2008 International Symposium on Software Testing and Analysis, New York, NY, USA, 2008, pp. 249-260. J. Offutt and A. Abdurazik, “Generating Tests from UML Specifications,” in Proceedings of the 2nd international conference on The unified modeling language: beyond the standard, Fort Collins, CO, USA, 1999, pp. 416-429. M. Schur, A. Roth, and A. Zeller, “ProCrawl: mining test models from multi-user web applications,” in Proceedings of the 2014 International Symposium on Software Testing and Analysis, San Jose, CA, USA, 2014, pp. 413-416. A. Mesbah, A. van Deursen, and D. Roest, “Invariant-Based Automatic Testing of Modern Web Applications,” IEEE Trans. Softw. Eng, vol. 38, iss. 1, pp. 35-53, Jan, 2012. A. Mesbah, A. van Deursen, and S. Lenselink, “Crawling Ajax-Based Web Applications Through Dynamic Analysis of User Interface State Changes,” ACM Trans. Web, vol. 6, iss. 1, p. 3:1–3:30, Mar. 2012. S. Pandey and C. Olston, “User-centric Web crawling,” in Proceedings of the 14th international conference on World Wide Web, Chiba, Japan, 2005, pp. 401-411. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, Lake Tahoe, Nevada, 2013, pp. 3111-3119. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” in Neural Comput, vol. 9, iss. 8, pp. 1735-1780, Nov. 1997. F. A. Gers, J. A. Schmidhuber, F. A. Cummins, “Learning to forget: Continual prediction with LSTM,” in Neural Comput, vol. 12, iss. 10, pp. 2451-2471, Oct. 2000. S. T. Dumais, “Latent Semantic Analysis,” in Annual Review of Information Science and Technology, 38(1), pp. 188-230. J-W. Lin, F. Wang, “Using Semantic Similarity in Crawling-Based Web Application Testing,” in 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST), Tokyo, Japan, 2017. L. E. Baum and T. Petrie, “Statistical Inference for Probabilistic Functions of Finite State Markov Chains” in The Annals of Mathematical Statistics, vol. 37, no. 6, pp.1554-1563, Dec. 1966. A.M. Martinez and A.C. Kak, “PCA versus LDA,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, iss. 2, pp. 228-233, Feb, 2001. X. Gu, H. Zhang, D. Zhang, and S. Kim, “Deep API learning,” in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, New York, NY, USA, 2016, pp. 631-642. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66798 | - |
| dc.description.abstract | 在軟體應用程式中，我們會依據畫面上內容的提示而決定我們該在上面執行輸入或點擊之類的操作，然而在進行大量網頁測試的工作時，如何自動化輸入或點擊畫面上的內容成為一個待解決的問題。本篇論文中，我們應用了自然語言處理、深度學習技術。首先根據目標應用程式元素的周圍元素去擷取其特徵，利用Word2Vec、長短期記憶法產生主題向量，藉由比較不同向量之間的相似度我們可以得到期相對應的主題。這些主題可以幫助我們去分析目標網頁元素是否可被執行，藉此來完成自動化測試。 | zh_TW |
| dc.description.abstract | In software applications, users can decide what kind of action they should perform such as fill in text or click elements according to hints on the contents of the screen. However, when doing a lot of application testing, how to automatically fill in text or clicking elements on the screen becomes a problem to be solved. In this thesis, we present a NLP (Natural Language Processing) with deep learning techniques. Firstly, according to surrounding elements of the target element, its feature would be retrieved. Secondly, we transfer the feature into topic vectors using Word2Vec model, Long Short-Term Memory methods. By comparing the similarities between different vectors, we can categorize them to their corresponding topics. These topics can help us tell if a target element can be executed or not, thereby achieving the automatic testing. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:08:33Z (GMT). No. of bitstreams: 1 ntu-109-R06943080-1.pdf: 1612915 bytes, checksum: 7b0179a3e2d7537968daf6f207207529 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES viii LIST OF ALGORITHMS ix Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Contribution 3 Chapter 2 Related Work 6 2.1 Manual Testing 6 2.2 Automated testing 6 2.2.1 Script-based testing 7 2.2.2 Model-based testing 7 2.2.3 Crawling-based testing 8 2.3 Academic Research 9 2.4 Summary 10 Chapter 3 Methodology 11 3.1 Natural language processing (NLP) 11 3.2 Word2Vec 13 3.2.1 CBOW model 14 3.2.2 Skip-gram model 14 3.3 Long short-term memory (LSTM) 15 3.4 Summary 18 Chapter 4 Topic generalization 19 4.1 DOM feature extraction 19 4.2 NLP and Wor2Vec transformation 25 4.3 LSTM vector transformation 25 4.4 LSTM model optimization 31 Chapter 5 Experiment 32 5.1 Test environment 32 5.1.1 TaaD 32 5.1.2 Experiment settings 32 5.2 Dataset 32 5.3 Accuracy 34 5.3.1 Effectiveness of Word2Vec model 34 5.3.2 Effectiveness of LSTM model 38 5.3.3 Prediction on target elements 40 5.4 Radius selection for feature distance 41 5.5 Comparison with NLP based technique 42 5.6 Summary 44 Chapter 6 Conclusion 45 REFERENCE 46 | |
| dc.language.iso | zh-TW | |
| dc.subject | 主題分析 | zh_TW |
| dc.subject | 長短期記憶 | zh_TW |
| dc.subject | Word2Vec | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 自動化測試 | zh_TW |
| dc.subject | 網頁測試 | zh_TW |
| dc.subject | Natural Language Processing | en |
| dc.subject | Automatic Testing | en |
| dc.subject | Topic Analysis | en |
| dc.subject | Web Testing | en |
| dc.subject | Word2Vec | en |
| dc.subject | deep learning | en |
| dc.subject | Long Short-Term Memory | en |
| dc.title | 利用深度學習技術自動化決定畫面元素主題 | zh_TW |
| dc.title | Automatic Topic Determination for Screen Elements Using Deep Learning Techniques | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 雷欽隆(Chin-Laung Lei),顏嗣鈞(Hsu-chun Yen),陳銘憲(Ming-Syan Chen),李宏毅(Hung-Yi Lee) | |
| dc.subject.keyword | 網頁測試,自動化測試,自然語言處理,深度學習,Word2Vec,長短期記憶,主題分析, | zh_TW |
| dc.subject.keyword | Web Testing,Automatic Testing,Natural Language Processing,deep learning,Word2Vec,Long Short-Term Memory,Topic Analysis, | en |
| dc.relation.page | 47 | |
| dc.identifier.doi | 10.6342/NTU202000255 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-02-03 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| Appears in Collections: | 電子工程學研究所 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-109-1.pdf Restricted Access | 1.58 MB | Adobe PDF |
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