SEL@KIT: K. Nishiura, E. Choi, and O. Mizuno, Improving Faulty Interaction Localization Using Logistic Regression, July 2017.
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K. Nishiura, E. Choi, and O. Mizuno, "Improving Faulty Interaction Localization Using Logistic Regression," In Proc. of the 2017 IEEE International Conference on Software Quality, Reliability & Security (QRS2017), pp. 138-149, July 2017.
ID 759
分類 国際会議(査読付)
タグ faulty improving interaction localization logistic regression
表題 (title) Improving Faulty Interaction Localization Using Logistic Regression
表題 (英文)
著者名 (author) Kinari Nishiura,Eun-Hye Choi,Osamu Mizuno
英文著者名 (author) Kinari Nishiura,Eun-Hye Choi,Osamu Mizuno
編者名 (editor)
編者名 (英文)
キー (key) Kinari Nishiura,Eun-Hye Choi,Osamu Mizuno
書籍・会議録表題 (booktitle) Proc. of the 2017 IEEE International Conference on Software Quality, Reliability & Security (QRS2017)
書籍・会議録表題(英文)
巻数 (volume)
号数 (number)
ページ範囲 (pages) 138-149
組織名 (organization)
出版元 (publisher)
出版元 (英文)
出版社住所 (address)
刊行月 (month) 7
出版年 (year) 2017
採択率 (acceptance) 25.77%
URL
付加情報 (note)
注釈 (annote)
内容梗概 (abstract) Combinatorial testing is a widely used technique to detect failures caused by interactions of system under test (SUT) parameters.
\emph{Faulty interaction localization (FIL)} is a problem to locate \pv combinations that trigger failures from combinatorial test cases and their testing results.
FIL is important for debugging, but is expensive for large test suites and SUTs since the number of candidates of faulty interactions increases exponentially with the number of parameters and the size of interactions.
To address this problem, this paper proposes a method employing \emph{logistic regression}.
The proposed \emph{FIL based on Regression coefficients Of loGistic regression analysis} (called \emph{FROG}) computes the suspiciousness of each parameter-value combination to be included in a faulty interaction from its corresponding regression coefficient.
We evaluate the proposed method by applying \FROG to combinatorial \T-way test cases ($2\leq t\leq 4$) for real application SUT models, \eg \tcas, \gcc, and \apache.
Our experiment results show that \FROG can effectively locate faulty interactions injected while efficiently reducing the number of candidates of potential faulty interactions to be checked.

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BiBTeXエントリ
@inproceedings{id759,
         title = {Improving Faulty Interaction Localization Using Logistic Regression},
        author = {Kinari Nishiura and Eun-Hye Choi and Osamu Mizuno},
     booktitle = {Proc. of the 2017 IEEE International Conference on Software Quality, Reliability \& Security (QRS2017)},
         pages = {138-149},
         month = {7},
          year = {2017},
    acceptance = {25.77\%},
}
  

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