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研究生:吳啟允
研究生(外文):Chi-Yun Wu
論文名稱:網頁和手機程式自動化測試智能技術
論文名稱(外文):Intelligent Techniques for Automated Testing of Web and Mobile Applications
指導教授:王凡
指導教授(外文):Farn Wang
口試委員:陳銘憲戴顯權蘇克毅楊得年
口試委員(外文):Ming-Syan ChenShen-Chuan TaiKeh-Yih SuDe-Nian Yang
口試日期:2016-07-25
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:39
中文關鍵詞:軟體測試網頁測試自動化測試測試準則
外文關鍵詞:Software testingWeb testingAutomated testingTest oracle
相關次數:
  • 被引用被引用:0
  • 點閱點閱:319
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
在網路普及的時代,各式各樣用途的網頁相繼開發出來,人們的生活也越來依靠各種網站。因此對開發者而言,為了確保網站程式的品質,如何快速有效的進行網頁測試就顯得重要。我們在此篇論文中開發了一套工具來自動化測試網頁,使用者不需撰寫測試腳本,就可以點擊和填值的方式自動瀏覽動態網頁,並且記錄下瀏覽的紀錄。然後我們提出了一個方法來建構測試準則,我們應用了機器學習中支撐向量機的技術,從網頁和手機的使用軌跡中抓取特徵值,順練出預測模型來自動判斷測試紀錄是否通過。藉由這個工具和驗證方法,我們可以有效地降低人力成本達到自動化測試的目的。

In the recent days, the Intertnet is becoming more popular. A wide range of web applications have been developed. People spent lots of time on Internet.
Thus, it becomes an important problem to verify the web applications to developers. In this paper, we propose a tool for automated web testing. The developers do not need to write the test scripts. The tool can explore the dynamic webpages by clicking buttons and insert values and record the test traces. We propose a system to construct test oracle of web applications and mobile applications using the support vector machines. The system extracts features from the traces and builds a redictive model to classify the passed traces and failed traces. With the automated testing tool and system,
we can effectively reduce human cost to achieve the purpose of automated testing.

口試委員會審定書i
誌謝ii
摘要iii
Abstract iv
1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Purpose 2
1.4 Organization 3
2 Related Works 4
2.1 Web testing 4
2.2 Test evaluation 5
3 Preliminaries 6
3.1 SpecElicitor 6
3.2 Normalized keywords 8
3.3 Support Vector Machine 9
4 WebTraceCollector 10
4.1 Framework 11
4.2 Webpage identification 13
4.3 Event 14
4.4 Suggested value 16
4.5 Normalization 17
4.6 Automata and traces 18
4.7 Trace collection 20
4.7.1 Monkey 20
4.7.2 Depth First Search 20
5 Trace Evaluation 22
5.1 Procedure 23
5.2 Feature vector 24
5.3 Sampling traces 26
6 Experiments 27
6.1 Trace collection 28
6.2 Dynamic webpages 31
6.3 Prediction 33
7 Conclusion 36
7.1 Summary 36
7.2 Limination 36
7.3 Future work 37
Bibliography 38

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[13] C. Cortes and V. Vapnik, “Support-vector networks,” in Machine learning 20(3), 1995, pp. 273–297.
[14] C.-C. Chang and C.-J. Lin, “Libsvm: A library for support vector machines,” in Acm transactions on intelligent systems and technology(tist), 2011, pp. 1–39.
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[18] B. Settles, “Active learning literature survey,”in Machine learning, 2010, pp. 201–221.
[19] Popular websites in taiwan. [Online]. Available: http://www.bnext.com.tw/article/view/id/35475.

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