Promise 2011: "Local Bias and its Impacts on the Performance of Parametric Estimation Models"
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This document discusses local bias in parametric estimation models and its impact on model performance. It defines local bias as the deviation between parameters calibrated from local data versus general model defaults. An analysis of a software cost estimation model finds local bias varies between data groups and is positively correlated with decreased model accuracy and increased uncertainty, as measured by mean and variance of magnitude of relative error. The implications are that local bias should be identified and addressed to improve model evolution and balance accuracy versus stability.
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Promise 2011: "Local Bias and its Impacts on the Performance of Parametric Estimation Models"
- 1. Local Bias and its Impacts on the Performance of Parametric Estimation Models Ye Yang, Lang Xie, Zhimin He (ISCAS) Qi Li, Vu Nguyen, Barry Boehm (USC) Ricardo Valerdi (MIT/Univ. of Arizona) Sep. 21, 2011 Promise 2011, Banff, Canada
- 10. Summary of Dataset CII 2000 Subset After2000 Subset CII 2010 Dataset
- 23. Thank you! Contact: Ye Yang (yangye@nfs.iscas.ac.cn)
Notes de l'éditeur
- These pictures show the stdMRE values and MMRE values in each data group.
- This table shows the results of correlation analysis. We can see that the range of stdMRE is significantly positive correlated with local bias and local_bias*num (loca bias times num). Both the average stdMRE and the average MMRE are significantly positive correlated with local_bias*num. Range of stdMRE reflects the uncertainty of model performance. So we argue that the bigger the local bias is, the weaker the model performance is.