Approach to cross-company spacecraft software defect prediction based on transfer learning

doi:10.3788/OPE.20192702.0469

CIOMP OpenIR

	Approach to cross-company spacecraft software defect prediction based on transfer learning
	Q.-H.Ha; D.-Y.Liu; Y.Chen; L.Liu
	2019
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	1004924X
卷号	27 期号:2 页码:469-478
摘要	In order to improve the efficiency and quality of aerospace software testing, an approach to cross-company aerospace software defect prediction was proposed, especially for the scarcity of within-company software and the long cycle of development. Considering the complexity, large scale, and independent functions of aerospace software, the idea of building a defect prediction model based on static classification was proposed. In this paper, the transfer learning method was introduced. Using the nearest neighbor classifier and data gravity model, the distribution characteristics of training data were corrected to improve the similarity between training data and target data. In order to improve the generalization ability of the model to adapt to the diversity of target data, a small amount of target data was added to the training data for model training. The approach was applied to the test for aerospace software testing. The results of application show that, compared with existing software defect prediction methods, the proposed method can effectively improve the recall rate (close to 0.6) with a low false alarm rate (not higher than 0.3). The overall credibility is effectively enhanced (G-measure is over 0.6), and the method has high stability and strong generalization ability. This method can control the test scale in practical projects and improve testing efficiency. 2019, Science Press. All right reserved.
关键词	Software testing,Aerospace engineering,Application programs,Classification (of information),Defects,Efficiency,Forecasting,Learning systems
DOI	10.3788/OPE.20192702.0469
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收录类别	EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/63351
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Q.-H.Ha,D.-Y.Liu,Y.Chen,et al. Approach to cross-company spacecraft software defect prediction based on transfer learning[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2019,27(2):469-478.
APA	Q.-H.Ha,D.-Y.Liu,Y.Chen,&L.Liu.(2019).Approach to cross-company spacecraft software defect prediction based on transfer learning.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,27(2),469-478.
MLA	Q.-H.Ha,et al."Approach to cross-company spacecraft software defect prediction based on transfer learning".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 27.2(2019):469-478.

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