local bias and its impacts on the performance of parametric estimation models
文献类型：会议论文
| 作者 | Yang Ye ; Xie Lang ; He Zhimin ; Li Qi ; Nguyen Vu ; Boehm Barry ; Valerdi Ricardo |
| 出版日期 | 2011 |
| 会议名称 | 7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011 |
| 会议日期 | September |
| 会议地点 | Banff, AB, Canada |
| 关键词 | Estimation Models Predictive control systems Software engineering |
| 页码 | - |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议录 | ACM International Conference Proceeding Series
 |
| 语种 | 英语 |
| ISBN号 | 9781450307093 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16228]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 见:7th International Conference on Predictive Models in Software Engineering, PROMISE 2011, Co-located with ESEM 2011. Banff, AB, Canada. September. |
入库方式： OAI收割
来源：软件研究所
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