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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 王凡(Farn Wang) | |
dc.contributor.author | Chung-Cheng Li | en |
dc.contributor.author | 黎忠政 | zh_TW |
dc.date.accessioned | 2021-06-16T16:32:03Z | - |
dc.date.available | 2018-01-16 | |
dc.date.copyright | 2013-01-16 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-12-07 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63274 | - |
dc.description.abstract | 我們設計一套系統,這套系統可以對手持裝置上的應用程式做軟體測試,且應用程式開發者不需提供程式原始碼,只需提供手持裝置的應用程式,我們即可對此應用程式做自動化測試。
這個系統可以擷取應用程式的資訊,也就是使用者在操作此應用程式也就是待測物時,我們可以記錄使用者在待測物上操作的事件。有了這些資訊,我們利用資料探勘的方式,找出這些事件的時序規則,而這些規則我們使用線性時序邏輯來表示。我們可以從這些線性時序規則對待測物做診斷,找出待測物有沒有任何可能發生的錯誤,最後再回報給應用程式開發者。 | zh_TW |
dc.description.abstract | In software engineering, specifications of a software are very important in comprehension,testing and verification the software. However, a well-documented and up-to-dated specification is expensive to construct and maintain. In recent year, automation to assist this issue has attracted many attentions.
In this work, we apply data mining approach for mining temporal rules of a software for diagnosis. We proposed a testing framework for testing Android applications in black-box fashion. By supplying the program execution traces, we can mine 3 types of temporal rules resident in the traces. Here we adopt Linear Temporal Logic as the specification language for it simplicity and great expressive power. Our experiments show that we can diagnosis the Android application with positive and negative traces and report the problems of Android applications. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T16:32:03Z (GMT). No. of bitstreams: 1 ntu-101-R99921078-1.pdf: 7832263 bytes, checksum: cfbe77bbe549e8e8c6553226b6940c40 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | Abstract ii
Acknowledgments iii List of Figures vi List of Tables vii 1 Introduction 1 2 Related Works 4 3 Preliminaries 6 3.1 Program Trace Model 6 3.2 Linear Temporal Logic 7 3.2.1 Syntax 8 3.2.2 Semantics 10 3.3 Association Rule Mining 11 4 The Framework 13 5 The Proposed Mining Algorithms 16 5.1 Mining Algorithm 17 5.2 Eliminating of positive properties from negative properties 23 6 Implementation 24 7 Experiment 26 7.1 Benchmarks 26 7.2 Data of Experiment . 27 7.3 Example of LTL Rules 28 7.3.1 Positive rule on Youtube 28 7.3.2 Negative Rule on Udn-News 29 8 Conclusions and Future Works 31 References 32 | |
dc.language.iso | en | |
dc.title | 探勘時序規則用以診斷 | zh_TW |
dc.title | Mining Temporal for Diagnosis | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 顏嗣鈞(Hsu-chun Yen),陳郁方(Yu-Fang Chen),郁方(Yu-Fang) | |
dc.subject.keyword | 軟體測試,自動化測試,規格探勘,資料探勘,線性時序邏輯, | zh_TW |
dc.subject.keyword | Data mining,Specification mining,Automatic testing,Black box,Android system, | en |
dc.relation.page | 33 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-12-07 | |
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-101-1.pdf 目前未授權公開取用 | 7.65 MB | Adobe PDF |
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