SEL@KIT: T. Fujiwara, O. Mizuno, and P. Leelaprute, Fault-Prone Byte-Code Detection Using Text Classifier, December 2015.
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T. Fujiwara, O. Mizuno, and P. Leelaprute, "Fault-Prone Byte-Code Detection Using Text Classifier," In Proc. of 16th International Conference on Product-Focused Software Process Improvement (PROFES2015), 1st International Workshop on Processes, Methods, and Tools for Engineering Embedded Systems, LNCS(9459), pp. 415-430, December 2015.
ID 714
分類 国際会議(査読付)
タグ byte-code classifier detection fault-prone text 国際共著
表題 (title) Fault-Prone Byte-Code Detection Using Text Classifier
表題 (英文)
著者名 (author) Tsuyoshi Fujiwara,Osamu Mizuno,Pattara Leelaprute
英文著者名 (author) Tsuyoshi Fujiwara,Osamu Mizuno,Pattara Leelaprute
編者名 (editor)
編者名 (英文)
キー (key) Tsuyoshi Fujiwara,Osamu Mizuno,Pattara Leelaprute
書籍・会議録表題 (booktitle) Proc. of 16th International Conference on Product-Focused Software Process Improvement (PROFES2015), 1st International Workshop on Processes, Methods, and Tools for Engineering Embedded Systems
書籍・会議録表題(英文)
巻数 (volume) LNCS
号数 (number) 9459
ページ範囲 (pages) 415-430
組織名 (organization)
出版元 (publisher)
出版元 (英文)
出版社住所 (address)
刊行月 (month) 12
出版年 (year) 2015
採択率 (acceptance)
URL
付加情報 (note) Bozen-Bolzano, Italy
注釈 (annote) 10.1007/978-3-319-26844-630
内容梗概 (abstract) Researchers have studied approaches to detect fault-prone modules for a long time. As one of these approaches, we proposed an approach using a text filtering technique. In this approach, we assume that faults relate to words and contexts in a software module. Our technique accepts inputs as a text information. Based on a dictionary that was learned by classifying modules that induce faults, the fault inducing probability over a target module is calculated, and it judges whether the given module is a fault-prone module.
Although our approach targeted the source code of software, especially in embedded software, the analysis of byte-code is also required. The source code based fault detection suffered from noises such as the way of writing, the used name of identifiers, and so on. Eliminating such noises may improve the accuracy of prediction. In this study, we aimed at fault detection from the byte-code of Java. Specifically, we tried to detect faults from the disassembled intermediate code of Java class file. To show the effectiveness of our approach, we conducted an experiment and compared our approach with source code based approach.
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BiBTeXエントリ
@inproceedings{id714,
         title = {Fault-prone Byte-code Detection Using Text Classifier},
        author = {Tsuyoshi Fujiwara and Osamu Mizuno and Pattara Leelaprute},
     booktitle = {Proc. of 16th International Conference on Product-Focused Software Process Improvement (PROFES2015), 1st International Workshop on Processes, Methods, and Tools for Engineering Embedded Systems},
        volume = {LNCS},
        number = {9459},
         pages = {415-430},
         month = {12},
          year = {2015},
          note = {Bozen-Bolzano, Italy},
        annote = {10.1007/978-3-319-26844-630},
}
  

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