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Title:
local bias and its impacts on the performance of parametric estimation models
Author: Yang Ye ; Xie Lang ; He Zhimin ; Li Qi ; Nguyen Vu ; Boehm Barry ; Valerdi Ricardo
Source: ACM International Conference Proceeding Series
Conference Name: 7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011
Conference Date: September
Issued Date: 2011
Conference Place: Banff, AB, Canada
Keyword: Estimation ; Models ; Predictive control systems ; Software engineering
Indexed Type: EI
ISBN: 9781450307093
Department: (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
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16228
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Yang Ye,Xie Lang,He Zhimin,et al. local bias and its impacts on the performance of parametric estimation models[C]. 见:7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011. Banff, AB, Canada. September.
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