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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王凡 | |
| dc.contributor.author | Cheng-Chieh Chang | en |
| dc.contributor.author | 張程傑 | zh_TW |
| dc.date.accessioned | 2021-06-16T16:04:19Z | - |
| dc.date.available | 2015-07-03 | |
| dc.date.copyright | 2013-07-03 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-06-27 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62553 | - |
| dc.description.abstract | We investigate how to use specification mining techniques for program anomaly analysis. We assume the input of positive traces (without execution anomalies) and negative traces (with execution anomalies).
We then partition the traces into the following clusters: a positive cluster that contains all positive traces and some negative clusters according to the characteristics of trace anomalies. We present techniques for learning temporal properties in Linear Temporal Logic with finite trace semantics (FLTL). We propose to mine FLTL properties that distinguish the negative clusters from the positive cluster. We present a method to learn the importance of FLTL properties for each cluster. We experiment with 5 Android applications from Google Code and Google Play with traces of GUI events and crashes as the target anomaly. The reported FLTL properties reveal the temporal patterns in GUI traces that cause the crashes. The performance data also shows that the clustering of negative traces indeed enhances the accuracy in mining meaningful temporal properties for test verdict prediction. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T16:04:19Z (GMT). No. of bitstreams: 1 ntu-102-R99943141-1.pdf: 2357168 bytes, checksum: 3f07c5cd00d63a1151842d197dc5f164 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | Abstract ii
List of Figures vi List of Tables vii 1 Introduction 1 2 Related Work 7 3 Preliminaries 10 3.1 Program Trace Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Finite Linear Temporal Logic . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Association Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.4 Crash Call Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4 The Framework 17 5 Rule Extraction 23 5.1 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.2 Target FLTL Templates . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.2.1 Nested Conditional Next Rule P1 . . . . . . . . . . . . . . . . 26 5.2.2 Nested Conditional Eventually Rule P2 . . . . . . . . . . . . . 27 5.2.3 Nested Eventually Rule P3 . . . . . . . . . . . . . . . . . . . . 28 5.2.4 Until Rule P4 . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5.2.5 Starvation Rule P5 . . . . . . . . . . . . . . . . . . . . . . . . 29 5.3 Mining Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.4 Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 6 Weight Learning and Accuracy Evaluation 33 6.1 Weight Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.2 Accuracy Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 7 Implementation 38 8 Experiment 42 8.1 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 8.2 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 43 8.3 Example of Mined Rules and Anomaly Analysis . . . . . . . . . . . . 47 8.3.1 Until Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 8.3.2 Starvation Rule . . . . . . . . . . . . . . . . . . . . . . . . . . 48 9 Conclusions and Future Works 51 References 52 | |
| dc.language.iso | en | |
| dc.subject | 規格探勘 | zh_TW |
| dc.subject | 程式軌跡 | zh_TW |
| dc.subject | 時態規則 | zh_TW |
| dc.subject | Specification Mining | en |
| dc.subject | FLTL | en |
| dc.subject | Clustering | en |
| dc.subject | Program trace | en |
| dc.subject | Android | en |
| dc.title | 從Android應用軌跡探勘時態規則於錯誤分析 | zh_TW |
| dc.title | Temporal Rules Mining from Android Application Traces for Anomaly Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 顏嗣鈞,黃鐘揚,郁方,王柏堯 | |
| dc.subject.keyword | 規格探勘,時態規則,程式軌跡, | zh_TW |
| dc.subject.keyword | Specification Mining,FLTL,Clustering,Program trace,Android, | en |
| dc.relation.page | 55 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-06-27 | |
| 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 |
| 顯示於系所單位： | 電子工程學研究所 | |
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