A Procedure for Analyzing Unbalanced Datasets
IEEE Transactions on Software Engineering
Calibrating the COCOMO II post-architecture model
Proceedings of the 20th international conference on Software engineering
Bayesian Analysis of Empirical Software Engineering Cost Models
IEEE Transactions on Software Engineering
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Preliminary Data Analysis Methods in Software Estimation
Software Quality Control
Evidence-Based Guidelines for Assessment of Software Development Cost Uncertainty
IEEE Transactions on Software Engineering
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
Cross versus Within-Company Cost Estimation Studies: A Systematic Review
IEEE Transactions on Software Engineering
Estimating software-intensive systems: projects, products, and processes
Estimating software-intensive systems: projects, products, and processes
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Exploiting the Essential Assumptions of Analogy-Based Effort Estimation
IEEE Transactions on Software Engineering
Journal of Systems and Software
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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.