[1]娄丰鹏,吴迪,荆晓远,等.增加度量元的迁移学习跨项目软件缺陷预测[J].计算机技术与发展,2018,28(07):103-107.[doi:10.3969/ j. issn.1673-629X.2018.07.022]
　LOU Feng-peng,WU Di,JING Xiao-yuan,et al.Cross-project Software Defect Prediction Based on Transfer Learning with Metrics[J].,2018,28(07):103-107.[doi:10.3969/ j. issn.1673-629X.2018.07.022]
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增加度量元的迁移学习跨项目软件缺陷预测()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年07期
页码:
103-107
栏目:
安全与防范
出版日期:
2018-07-10

文章信息/Info

Title:
Cross-project Software Defect Prediction Based on Transfer Learning with Metrics
文章编号:
1673-629X(2018)07-0103-05
作者:
娄丰鹏1 ; 吴迪2 ; 荆晓远3 ; 吴飞3
1. 南京邮电大学 计算机学院,江苏 南京 210003;
2. 武汉大学 计算机学院 软件工程国家重点实验室,湖北 武汉 430072;
3. 南京邮电大学 自动化学院,江苏 南京 210003
Author(s):
LOU Feng-peng 1 ; WU Di 2 ; JING Xiao-yuan 3 ; WU Fei 3
1. School of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. State Key Laboratory of Software Engineering,School of Computer,Wuhan University,Wuhan 430072,China;
3. School of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
跨项目; 机器学习; 软件缺陷预测; 迁移学习; 分类器
Keywords:
cross-project; machine learning; software defect prediction; transfer learning; classifier
分类号:
TP301.6
DOI:
10.3969/ j. issn.1673-629X.2018.07.022
文献标志码:
A
摘要:
目前,结合机器学习方法和软件缺陷预测技术自动地学习模型来发现软件中的缺陷,已经成为跨项目缺陷预测的主要方法。由于源项目和目标项目之间的特征分布差异,跨项目相关性预测的表现通常较差。针对该问题,可以使用从源项目中提取知识并将其转移到目标项目的转移学习技术来提高预测性能,并提出了一种增加度量元的迁移学习方法进行跨项目的软件缺陷预测。该方法首先使用分类器对数据集进行一次项目内预测,并将预测结果作为新的度量元加入数据集。然后采用迁移学习方法将源项目中提取的知识转移至目标项目,并使用分类器预测目标项目。在AEEEM 数据集上的实验结果表明,该算法提高了跨项目软件缺陷预测效率。
Abstract:
The combination of machine learning method and software defect prediction technology to automatically learn the model to find the software defects has become the main method of cross-project defect prediction in recent years. However,the performance of crossproject prediction is generally poor largely due to feature distribution differences between the source and target projects. In this cases,we propose a method to add the prediction results as a metric to the original data set to form a new data set for cross-project software defect prediction. First,a classifier is used to predict the data set in a project,and the predicted result is added as a new metric to the data set. Second,a transfer learning method is applied to transfer knowledge from original source project to target project,and a classifier is used to predict target project,thus effectively improving the prediction accuracy. The experiment on AEEEM data set shows that the proposed method significantly improves cross-project prediction performance.

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更新日期/Last Update: 2018-08-29