Can requirements dependency network be used as early indicator of software integration bugs?

ISCAS OpenIR

	Can requirements dependency network be used as early indicator of software integration bugs?
	Wang, Junjie (1); Li, Juan (1); Wang, Qing (1); Yang, Da (1); Zhang, He (4); Li, Mingshu (1)
	2013
会议名称	2013 21st IEEE International Requirements Engineering Conference, RE 2013
页码	185-194
会议日期	July 15, 2013 - July 19, 2013
会议地点	Rio de Janeiro, Brazil
收录类别	EI
出版地	IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States
ISBN	9781467357654
部门归属	(1) Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) University of Chinese Academy of Sciences, Beijing, China; (3) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; (4) School of Architecture, Computing and Engineering, University of East London, United Kingdom
摘要	Complexity cohesion and coupling have been recognized as prominent indicators for software quality. One characterization of software complexity is the existence of dependency relationship. Moreover, degree of dependency reflects the cohesion and coupling between software elements. Dependencies on design and implementation phase have been proven as important predictors for software bugs. We empirically investigated how requirements dependencies correlate with and predict software integration bugs, which can provide early estimate regarding software quality, therefore facilitate decision making early in the software lifecycle. We conducted network analysis on requirements dependency networks of two commercial software projects. We then performed correlation analysis between network measures (e.g., degree, closeness) and number of bugs. Afterwards, bug prediction models were built using these network measures. Significant correlation is observed between most of our network measures and number of bugs. These network measures can predict the number of bugs with high accuracy and sensitivity. We further identified the significant predictors for bug prediction. Besides, the indication effect of network measures on bug number varies among different types of requirements dependency. These observations show that requirements dependency network can be used as an early indicator of software Integration bugs, © 2013 IEEE.; Complexity cohesion and coupling have been recognized as prominent indicators for software quality. One characterization of software complexity is the existence of dependency relationship. Moreover, degree of dependency reflects the cohesion and coupling between software elements. Dependencies on design and implementation phase have been proven as important predictors for software bugs. We empirically investigated how requirements dependencies correlate with and predict software integration bugs, which can provide early estimate regarding software quality, therefore facilitate decision making early in the software lifecycle. We conducted network analysis on requirements dependency networks of two commercial software projects. We then performed correlation analysis between network measures (e.g., degree, closeness) and number of bugs. Afterwards, bug prediction models were built using these network measures. Significant correlation is observed between most of our network measures and number of bugs. These network measures can predict the number of bugs with high accuracy and sensitivity. We further identified the significant predictors for bug prediction. Besides, the indication effect of network measures on bug number varies among different types of requirements dependency. These observations show that requirements dependency network can be used as an early indicator of software Integration bugs, © 2013 IEEE.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16639
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Wang, Junjie ,Li, Juan ,Wang, Qing ,et al. Can requirements dependency network be used as early indicator of software integration bugs?[C]. IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States,2013:185-194.