篇名 |
An Integrated Semi-supervised Software Defect Prediction Model
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並列篇名 | An Integrated Semi-supervised Software Defect Prediction Model |
作者 | Fanqi Meng、Wenying Cheng、Jingdong Wang |
英文摘要 | A novel semi-supervised software defect prediction model FFeSSTri (Filtered Feature Selecting, Sample and Tri-training) is proposed to address the problem that class imbalance and too many irrelevant or redundant features in labelled samples lower the accuracy of semi-supervised software defect prediction. Its innovation lies in that the construction of FFeSSTri integrates an oversampling technique, a new feature selection method, and a Tri-training algorithm, thus it can effectively improve the accuracy. Firstly, the oversampling technique is applied to expand the class of inadequate samples, thus it solves the unbalanced classification of the labelled samples. Secondly, a new filtered feature selection method based on relevance and redundancy is proposed, which can exclude those irrelevant or redundant features from labelled samples. Finally, the Tri-training algorithm is used to learn the labelled training samples to build the defect prediction model FFeSSTri. The experiments conducted on the NASA software defect prediction dataset show that FFeSSTri outperforms the existing four supervised learning methods and one semi-supervised learning method in terms of F-Measure values and AUC values.
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起訖頁 | 1307-1317 |
關鍵詞 | Software defect prediction、Semi-supervised learning、Feature selection、Unbalanced classification、Oversampling techniques |
刊名 | 網際網路技術學刊 |
期數 | 202311 (24:6期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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