Measuring the Heterogeneity of Crosscompany Datasets
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Measuring the Heterogeneity of Crosscompany Datasets
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2010
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Abstract
As a standard practice, general effort estimate models are
calibrated from large cross-company datasets. However, many of
the records within such datasets are taken from companies that
have calibrated the model to match their own local practices.
Locally calibrated models are a double-edged sword; they often
improve estimate accuracy for that particular organization, but
they also encourage the growth of local biases. Such biases
remain present when projects from that firm are used in a new
cross-company dataset. Over time, such biases compound, and the
reliability and accuracy of a general model derived from the data
will be affected by the increased level of heterogeneity. In this
paper, we propose a statistical measure of the exact level of
heterogeneity of a cross-company dataset. In experimental tests,
we measure the heterogeneity of two COCOMO-based datasets
and demonstrate that one is more homogeneous than the other.
Such a measure has potentially important implications for both
model maintainers and model users. Furthermore, a heterogeneity
measure can be used to inform users of the appropriate data
handling techniques.
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Associated research group: Critical Systems Research Group
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PROFES 2010
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Chen, Jia; Yang, Ye; Zhang, Wen; Gay, Gregory. (2010). Measuring the Heterogeneity of Crosscompany Datasets. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217411.
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