月旦知識庫
  1. 熱門:
 
首頁 臺灣期刊   法律   公行政治   醫學   財經   社會學   教育   其他 大陸期刊   核心   非核心 DOI文章
篇名
BTHYDRA+: Towards a Comprehensive Model to Conduct Cross-project Defect Prediction
並列篇名
BTHYDRA+: Towards a Comprehensive Model to Conduct Cross-project Defect Prediction
作者 Ning HuangJinglong FangDan WeiBin ChenXingqi Wang
中文摘要
Cross-project defect prediction (CPDP) is a field of study where researchers need to build a universal model by using within-project data to predict defects on other projects. However, variations in the distribution of source and target projects have an influence on the performance of classifiers. To enable effective cross-project defect prediction, we propose a comprehensive model containing data preprocessing and classifier transferring to construct a better classification space and strengthen the performance of classifiers. In preprocessing step, one baseline is calculated for every dataset from its non-defective samples based on the distance to all other non-defective samples and the data is transformed by using rank function. Genetic algorithm and ensemble learning are selected as the way in transferring step to extract effective representation from source projects and boost the capability of weak classifiers. We use Naive Bayes, Support Vector Machine and Classification and Regression Trees as classifiers and apply this model on five open resource projects (one Apache and four Eclipse projects) and NASA MDP dataset. Selecting accuracy as fitness in genetic algorithm improves the performance of classification. The model we proposed yields similar results and obtains higher precision comparing the within-project models. Meanwhile, it obtains better performance than the state-of- the-art methods on cross-project defect prediction. These results show that our model provides an opportunity to training classifiers by using more samples from different projects.
起訖頁 052-065
關鍵詞 classifier transferringcross-project defect predictiondata preprocessing
刊名 電腦學刊  
期數 201812 (29:6期)
該期刊-上一篇 Vertical Handoff Algorithm for Vehicle Heterogeneous Network Based on Motion Trend Prediction
該期刊-下一篇 Multi-instance Combination Decision in Infrastructure-as-a-Service Cloud under Cloud Users’ Demands Fluctuation
 

新書閱讀



最新講座


優惠活動




讀者服務專線:+886-2-23756688 傳真:+886-2-23318496
地址:臺北市館前路28 號 7 樓 客服信箱
Copyright © 元照出版 All rights reserved. 版權所有,禁止轉貼節錄