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| H. Hata, O. Mizuno, and T. Kikuno, "An Extension of Fault-Prone Filtering Using Precise Training and a Dynamic Threshold," In Proc. of 5th Working Conference on Mining Software Repositories (MSR2008), pp. 89-97, May 2008. | |
| ID | 527 |
| 分類 | 国際会議(査読付) |
| タグ | dynamic extension fault-prone filtering precise threshold training major-mizuno full-paper |
| 表題 (title) |
An Extension of Fault-Prone Filtering Using Precise Training and a Dynamic Threshold |
| 表題 (英文) |
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| 著者名 (author) |
Hideaki Hata,Osamu Mizuno,Tohru Kikuno |
| 英文著者名 (author) |
Hideaki Hata,Osamu Mizuno,Tohru Kikuno |
| 編者名 (editor) |
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| 編者名 (英文) |
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| キー (key) |
Hideaki Hata,Osamu Mizuno,Tohru Kikuno |
| 書籍・会議録表題 (booktitle) |
Proc. of 5th Working Conference on Mining Software Repositories (MSR2008) |
| 書籍・会議録表題(英文) |
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| 巻数 (volume) |
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| 号数 (number) |
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| ページ範囲 (pages) |
89-97 |
| 組織名 (organization) |
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| 出版元 (publisher) |
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| 出版元 (英文) |
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| 出版社住所 (address) |
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| 刊行月 (month) |
5 |
| 出版年 (year) |
2008 |
| 採択率 (acceptance) |
19% |
| URL |
http://portal.acm.org/citation.cfm?id=1370772 |
| 付加情報 (note) |
Leipzig, Germany |
| 注釈 (annote) |
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| 内容梗概 (abstract) |
Fault-prone module detection in source code is important for assurance of software quality. Most previous fault-prone detection approaches have been based on software metrics. Such approaches, however, have difficulties in collecting the metrics and in constructing mathematical models based on the metrics. To mitigate such difficulties, we have proposed a novel approach for detecting fault-prone modules using a spam-filtering technique, named Fault-Prone Filtering. In our approach, fault-prone modules are detected in such a way that the source code modules are considered as text files and are applied to the spam filter directly. In practice, we use the training only errors procedure and apply this procedure to fault-prone. Since no pre-training is required, this procedure can be applied to an actual development field immediately. This paper describes an extension of the training only errors procedures. We introduce a precise unit of training, modified lines of code, instead of methods. In addition, we introduce the dynamic threshold for classification. The result of the experiment shows that our extension leads to twice the precision with about the same recall, and improves 15% on the best F1 measurement. |
| 論文電子ファイル | 利用できません． |
| BiBTeXエントリ |
@inproceedings{id527,
title = {An Extension of Fault-Prone Filtering Using Precise Training and A Dynamic Threshold},
author = {Hideaki Hata and Osamu Mizuno and Tohru Kikuno},
booktitle = {Proc. of 5th Working Conference on Mining Software Repositories (MSR2008)},
pages = {89-97},
month = {5},
year = {2008},
acceptance = {19\%},
note = {Leipzig, Germany},
}
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