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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王凡(Farn Wang) | |
| dc.contributor.author | Wen-Chi, Hung | en |
| dc.contributor.author | 洪文起 | zh_TW |
| dc.date.accessioned | 2021-05-13T06:40:40Z | - |
| dc.date.available | 2017-07-20 | |
| dc.date.available | 2021-05-13T06:40:40Z | - |
| dc.date.copyright | 2017-07-20 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-17 | |
| dc.identifier.citation | 1. Android Studio: the Monkey tester. https://developer.android.com/studio/ test/monkey.html (2017) Accessed: 2017-01-08.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2474 | - |
| dc.description.abstract | 此篇論文應用機率近似模型學習演算法學習多樣性自動機。該演算法會產生目標軟體之多樣性自動機模型，並將其應用於量化分析。使用產生之多樣性自動機模型，我們設計了一系列演算法可以預測軟體量化分析目標軟體的最大值與平均值等特性。此外，我們修改該演算法，使其在輸入字母數量並非固定時也可以通用。在此片論文中我們實際測試了五種不同類型的軟體，實驗證明我們預測的結果與暴力法算得之結果相當接近，足以證明此演算法可以產生一些傳統難以預測的數據，並具有相當高的可信度。 | zh_TW |
| dc.description.abstract | In this paper, we apply a probably approximately correct (PAC) learning algorithm for multiplicity automata which can generate a quantitative model of target system behaviors with a statistical guarantee. By using the generated multiplicity automata model, we apply two analysis algorithms to estimate the minimum, maximum and average values of system behaviors. Also, we demonstrate how to apply the learning algorithm when the alphabet symbol size is not fixed. The result of the experiment is encouraging; Our approach made the estimation which is as precise as the exact reference answer obtains by a brute force enumeration. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-13T06:40:40Z (GMT). No. of bitstreams: 1 ntu-106-R04921051-1.pdf: 1859531 bytes, checksum: 7bbe792e78b0baadf0a81322abb3dd2a (MD5) Previous issue date: 2017 | 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 Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Related Work 3 1.3 Contribution 4 Chapter 2 Preliminaries 5 2.1 Multiplicity Automata 5 2.2 Hankel Matrix 6 Chapter 3 Learning Algorithm of MA 7 3.1 PAC Learning 7 Chapter 4 Overview 10 Chapter 5 Analyzing Properties of MA 12 5.1 Computing the min. of 13 5.2 Computing the average of 14 Chapter 6 Optimizations 15 6.1 Learning the alphabet symbols incrementally 15 6.2 Double check the learned min./max. value 16 Chapter 7 Running Example: Calculator 17 Chapter 8 Calculator Experiment 21 8.1 Calculator 21 8.2 Considerations on the choice of distribution 22 8.3 Incremental alphabet refinement 23 8.4 Distribution of the execution time 23 Chapter 9 Operating System Scheduling Experiment 25 Chapter 10 Missionaries and Cannibals Experiment 26 Chapter 11 Amount of Data Transmission in a Website 28 Summary 29 REFERENCE 30 | |
| dc.language.iso | en | |
| dc.title | 多樣性自動機之學習演算法與其量化分析 | zh_TW |
| dc.title | Quantitative Analysis using Multiplicity Automata Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 雷欽隆(Chin-Laung Lei),陳銘憲(Ming-Syan Chen),陳郁方(Yu-Fang Chen),江介宏(Jie-Hong Jiang) | |
| dc.subject.keyword | 軟體測試,機率近似正確,機器學習,多樣性自動機,量化分析, | zh_TW |
| dc.subject.keyword | software testing,probably approximately correct,machine learning,quantitative analysis,multiplicity automata, | en |
| dc.relation.page | 31 | |
| dc.identifier.doi | 10.6342/NTU201701657 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2017-07-18 | |
| 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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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-106-1.pdf | 1.82 MB | Adobe PDF | 檢視/開啟 |
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