請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54364完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王凡(Farn Wang) | |
| dc.contributor.author | Ting-Fen Wu | en |
| dc.contributor.author | 吳庭棻 | zh_TW |
| dc.date.accessioned | 2021-06-16T02:52:43Z | - |
| dc.date.available | 2020-07-20 | |
| dc.date.copyright | 2015-07-20 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-07-13 | |
| dc.identifier.citation | [1] Andy’s PHP Knowledgebase Project. http://aphpkb.org/.
[2] Apache JMeter. http://jmeter.apache.org/. [3] Crawljax: crawling ajax-based web applications. http://crawljax.com/. [4] SchoolMate: PHP/MySQL solution for elementary, middle and high schools.http://sourceforge.net/projects/schoolmate/. [5] Selenium: web browser automation. http://www.seleniumhq.org/. [6] The W3C Markup Validation Service. https://validator.w3.org/. [7] S. Artzi, A. Kiezun, J. Dolby, F. Tip, D. Dig, A. Paradkar, and M. D. Ernst. Finding bugs in dynamic web applications. Proceedings of the 2008 international symposium on Software testing and analysis ISSTA 08, page 261, 2008. [8] E. T. Barr, M. Harman, P. Mcminn, M. Shahbaz, and S. Yoo. The Oracle Problem in Software Testing : A Survey. IEEE Transactions on Software Engineering, 41(5):507 – 525, 2015. [9] M. Benedikt, J. Freire, and P. Godefroid. VeriWeb: Automatically testing dynamic web sites. 11th Int. Conf. on World Wide Web (WWW02), 2002. [10] C.-c. Chang and C.-j. Lin. LIBSVM : A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2:1–39, 2011. [11] C. Cortes and V. Vapnik. Support-Vector Networks. Machine Learning, 20(3):273–297, 1995. [12] M. D. Ernst, J. Cockrell, W. G. Griswold, D. Notkin, and I. C. Society. Dynamically Discovering Likely Program Invariants to Support Program Evolution. IEEE Transactions on Software Engineering, 27(2):99–123, 2001. [13] S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu. Batch mode active learning and its application to medical image classification. Proceedings of the 23rd International Conference on Machine learning - ICML ’06, pages 417–424, 2006. [14] U. Kanewala and J. M. Bieman. Using machine learning techniques to detect metamorphic relations for programs without test oracles. 2013 IEEE 24th International Symposium on Software Reliability Engineering, ISSRE 2013, pages 1–10, 2013. [15] J. Kang, K. R. Ryu, and H.-C. Kwon. Using cluster-based sampling to select initial training set for active learning in text classification. Advances in knowledge discovery and data mining, pages 384–388, 2004. [16] a. Marchetto, a. Marchetto, P. Tonella, P. Tonella, F. Kessler-IRST, and F. Kessler-IRST. Search-Based Testing of Ajax Web Applications. Doi.Ieeecomputersociety.Org, 2009. [17] A. Mesbah, A. Van Deursen, and D. Roest. Invariant-based automatic testing of modern web applications. IEEE Transactions on Software Engineering, 38(1):35–53, 2012. [18] M. Mirzaaghaei and A. Mesbah. DOM-Based Test Adequacy Criteria for Web Applications. Issta, pages 71–81, 2014. [19] F. Ricca and P. Tonella. Analysis and testing of Web applications. Proceedings of the 23rd International Conference on Software Engineering ICSE 2001, 47(6):25–34, 2001. [20] B. Settles. Active Learning Literature Survey. Machine Learning, 15(2):201–221, 2010. [21] Y. Zou, Z. Chen, Y. Zheng, X. Zhang, and Z. Gao. Virtual DOM Coverage : Drive an Effective Testing for Dynamic Web Applications Categories and Subject Descriptors. Proceedings of the 2014 international symposium on Software testing and analysis ISSTA 14, pages 60–70, 2014. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54364 | - |
| dc.description.abstract | 在自動化測試中，自動生成測試準則扮演一個十分重要的腳色。在軟體測試中，通常一開始不會有測試準則來驗證系統的行為是否是符合預期，測試者必須一項一項去檢查系統的每個行為有沒有異常，這是一項十分繁重又耗時的工作。在這篇論文中，我們提出了一個系統可以有效率地建構網頁程式的測試準則，我們應用了機器學習領域中，主動學習法和支撐向量機的技術。這個系統會從網頁程式的執行軌跡中抓取特徵值，用很小的資料量去訓練出預測模型來判斷這些系統行為是否通過。我們的系統應用了主動學習法和一些抽樣策略來減少人力測試驗證的成本，而且仍可得到高準確率的預測結果。 | zh_TW |
| dc.description.abstract | Test oracle automation plays an important role in test automation. Many programs don’t have an oracle at the beginning of the testing, and the tester should verify all the software behaviors to check whether they are correct. Such the work is too heavy and time-consuming. In this paper, we present a efficient system to construct test oracle of the web applications using active learning and support vector machines. The system extracts the features of execution traces, then builds a predictive model to classify the passed traces and failed traces with a small training set. Our approach is reducing the human oracle cost by active learning and sampling strategies, and get high accuracy of predicted labels. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T02:52:43Z (GMT). No. of bitstreams: 1 ntu-104-R02921045-1.pdf: 2203145 bytes, checksum: fad30efa832cc9e5f60cc30f8a0277b2 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | Acknowledgment ii
Abstract in Chinese iii Abstract iv Contents v List of Figures vii List of Tables viii 1 Introduction 1 2 Related Work 4 2.1 Web Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Test Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Methodology 7 3.1 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Active Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 Test Oracle Learning System 10 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 v4.2 Trace Collector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.1 Crawler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3 Learner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.3.1 Active Learning Procedure . . . . . . . . . . . . . . . . . . . . . 14 4.3.2 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3.3 Sampling Strategy . . . . . . . . . . . . . . . . . . . . . . . . . 19 5 Experimental Result 23 5.1 State-Feature Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Keyword-Feature Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 6 Conclusion 32 Bibliography 34 Curriculum Vitae 37 | |
| dc.language.iso | en | |
| dc.subject | 軟體測試 | zh_TW |
| dc.subject | 網頁程式 | zh_TW |
| dc.subject | 主動學習法 | zh_TW |
| dc.subject | 主動學習法 | zh_TW |
| dc.subject | 測試準則 | zh_TW |
| dc.subject | 測試準則 | zh_TW |
| dc.subject | 軟體測試 | zh_TW |
| dc.subject | 網頁程式 | zh_TW |
| dc.subject | web application | en |
| dc.subject | software testing | en |
| dc.subject | web application | en |
| dc.subject | active learning | en |
| dc.subject | test oracle | en |
| dc.subject | software testing | en |
| dc.subject | active learning | en |
| dc.subject | test oracle | en |
| dc.title | 使用主動學習法建構網頁程式之測試準則 | zh_TW |
| dc.title | Constructing Test Oracle for Web Applications with Active Learning Techniques | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭大維,戴顯權,陳郁方,王柏堯 | |
| dc.subject.keyword | 軟體測試,網頁程式,主動學習法,測試準則, | zh_TW |
| dc.subject.keyword | software testing,web application,active learning,test oracle, | en |
| dc.relation.page | 37 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-07-13 | |
| 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-104-1.pdf 未授權公開取用 | 2.15 MB | Adobe PDF |
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