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Effort estimation often requires generalizing from a small number of historical projects. Generalization from such limited experience is an inherently underconstrained problem. Hence, the learned effort models can exhibit large deviations that prevent standard statistical methods (e.g., t-tests) from distinguishing the performance of alternative effort-estimation methods. The COSEEKMO effort-modeling workbench applies a set of heuristic rejection rules to comparatively assess results from alternative models. Using these rules, and despite the presence of large deviations, COSEEKMO can rank alternative methods for generating effort models. Based on our experiments with COSEEKMO, we advise a new view on supposed "best practices” in model-based effort estimation: 1) Each such practice should be viewed as a candidate technique which may or may not be useful in a particular domain, and 2) tools like COSEEKMO should be used to help analysts explore and select the best method for a particular domain.
Model-based effort estimation, COCOMO, deviation, data mining.

Z. Chen, T. Menzies, J. Hihn and K. Lum, "Selecting Best Practices for Effort Estimation," in IEEE Transactions on Software Engineering, vol. 32, no. , pp. 883-895, 2006.
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