incorporating qualitative and quantitative factors for software defect prediction

ISCAS OpenIR

	incorporating qualitative and quantitative factors for software defect prediction
	Wang Dandan; Wang Qing; Hong Zhenghua; Chen Xichang; Zhang Liwen; Yang Ye
	2012
会议名称	2nd International Workshop on Evidential Assessment of Software Technologies, EAST 2012
会议录名称	EAST'12 - Proceedings of the 2nd International Workshop on Evidential Assessment of Software Technologies
页码	61-65
会议日期	September 22, 2012 - September 22, 2012
会议地点	Lund, Sweden
收录类别	EI
ISBN	9781450315098
部门归属	(1) Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China; (2) Graduate University Chinese Academy of Sciences Beijing 100190 China; (3) Chinese Development Bank Beijing 100037 China
摘要	Defect is an important quality attribute of software. Defect is injected in development process and depended on the maturity level of the processes. How many defects were detected is enough? In any software organization, effort estimation and defect prediction are big challenges. Predicting the number of defects in the early stage of software development life cycle will be more helpful for the organizations to estimate the quality of developed product and optimize the resources schedule. Especially in outsourcing organization, the early precise defect prediction can help them to monitor the supplier's process and establish the criteria to verify the outsourcing products. Chinese development bank (CDB) is such an outsourcing organization, who applied SAM process area of CMMI to manage their outsourcing projects. In this paper, we proposed a prediction mode, which incorporated the qualitative factors from COQUALMO and the quantitative data collected from 21 historic financial projects of CDB. Principal Component Analysis (PCA) method was adopted to analyze the inter-correlated factors, and the key factors were determined to simplify the proposed model. We also evaluated its performance and compared with the software defect introduction (DI) model of COQUALMO. The results show that 66.67% predicted results are better than DI model and 80.5% predicted results have AE which are less than 50 while 95.24% predicted results have AE which are less than 100. Copyright 2012 ACM.; Defect is an important quality attribute of software. Defect is injected in development process and depended on the maturity level of the processes. How many defects were detected is enough? In any software organization, effort estimation and defect prediction are big challenges. Predicting the number of defects in the early stage of software development life cycle will be more helpful for the organizations to estimate the quality of developed product and optimize the resources schedule. Especially in outsourcing organization, the early precise defect prediction can help them to monitor the supplier's process and establish the criteria to verify the outsourcing products. Chinese development bank (CDB) is such an outsourcing organization, who applied SAM process area of CMMI to manage their outsourcing projects. In this paper, we proposed a prediction mode, which incorporated the qualitative factors from COQUALMO and the quantitative data collected from 21 historic financial projects of CDB. Principal Component Analysis (PCA) method was adopted to analyze the inter-correlated factors, and the key factors were determined to simplify the proposed model. We also evaluated its performance and compared with the software defect introduction (DI) model of COQUALMO. The results show that 66.67% predicted results are better than DI model and 80.5% predicted results have AE which are less than 50 while 95.24% predicted results have AE which are less than 100. Copyright 2012 ACM.
关键词	Forecasting Outsourcing Principal Component Analysis
主办者	ACM Special Interest Group on Software Engineering (SIGSOFT); IEEE Computer Society (CS)
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15874
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Wang Dandan,Wang Qing,Hong Zhenghua,et al. incorporating qualitative and quantitative factors for software defect prediction[C],2012:61-65.