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http://dx.doi.org/10.7236/JIIBC.2018.18.3.151

Severity-based Fault Prediction using Unsupervised Learning  

Hong, Euyseok (Dept. of Information Systems Engineering, Sungshin Women's University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.18, no.3, 2018 , pp. 151-157 More about this Journal
Abstract
Most previous studies of software fault prediction have focused on supervised learning models for binary classification that determines whether an input module has faults or not. However, binary classification model determines only the presence or absence of faults in the module without considering the complex characteristics of the fault, and supervised model has the limitation that it requires a training data set that most development groups do not have. To solve these two problems, this paper proposes severity-based ternary classification model using unsupervised learning algorithms, and experimental results show that the proposed model has comparable performance to the supervised models.
Keywords
Fault prediction; Defect severity; Unsupervised learning;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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