local bias and its impacts on the performance of parametric estimation models

ISCAS OpenIR

	local bias and its impacts on the performance of parametric estimation models
	Yang Ye; Xie Lang; He Zhimin; Li Qi; Nguyen Vu; Boehm Barry; Valerdi Ricardo
	2011
会议名称	7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011
会议录名称	ACM International Conference Proceeding Series
页码	-
会议日期	September
会议地点	Banff, AB, Canada
收录类别	EI
ISBN	9781450307093
部门归属	(1) Lab for Internet Software Technology Institute of Software Chinese Academy of Sciences Beijing China; (2) Graduate University of Chinese Academy of Sciences Beijing China; (3) Center for Systems and Software Engineering University of Southern California Los Angeles United States; (4) Lean Advancement Initiative Massachusetts Institute of Technology Cambridge United States
摘要	Background: Continuously calibrated and validated parametric models are necessary for realistic software estimates. However, in practice, variations in model adoption and usage patterns introduce a great deal of local bias in the resultant historical data. Such local bias should be carefully examined and addressed before the historical data can be used for calibrating new versions of parametric models. Aims: In this study, we aim at investigating the degree of such local bias in a cross-company historical dataset, and assessing its impacts on parametric estimation model's performance. Method: Our study consists of three parts: 1) defining a method for measuring and analyzing the local bias associated with individual organization data subset in the overall dataset; 2) assessing the impacts of local bias on the estimation performance of COCOMO II 2000 model; 3) performing a correlation analysis to verify that local bias can be harmful to the performance of a parametric estimation model. Results: Our results show that the local bias negatively impacts the performance of parametric model. Our measure of local bias has a positive correlation with the performance by statistical importance. Conclusion: Local calibration by using the whole multi-company data would get worse performance. The influence of multi-company data could be defined by local bias and be measured by our method.Copyright © 2011 ACM.; Background: Continuously calibrated and validated parametric models are necessary for realistic software estimates. However, in practice, variations in model adoption and usage patterns introduce a great deal of local bias in the resultant historical data. Such local bias should be carefully examined and addressed before the historical data can be used for calibrating new versions of parametric models. Aims: In this study, we aim at investigating the degree of such local bias in a cross-company historical dataset, and assessing its impacts on parametric estimation model's performance. Method: Our study consists of three parts: 1) defining a method for measuring and analyzing the local bias associated with individual organization data subset in the overall dataset; 2) assessing the impacts of local bias on the estimation performance of COCOMO II 2000 model; 3) performing a correlation analysis to verify that local bias can be harmful to the performance of a parametric estimation model. Results: Our results show that the local bias negatively impacts the performance of parametric model. Our measure of local bias has a positive correlation with the performance by statistical importance. Conclusion: Local calibration by using the whole multi-company data would get worse performance. The influence of multi-company data could be defined by local bias and be measured by our method.Copyright © 2011 ACM.
关键词	Estimation Models Predictive Control Systems Software Engineering
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16228
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Yang Ye,Xie Lang,He Zhimin,et al. local bias and its impacts on the performance of parametric estimation models[C],2011:-.