| 摘要 |
A method of reoptimizing the coefficients of a similarity function coefficient estimation as mavericks are resolved in a maverick analysis comprises computing initial weights for each feature and passing the similarity function to an estimation procedure, along with software objects, their group assignments, a peer parameter K and a confidence parameter N. Receiving as output and using updated values for the coefficients to obtain lists of misclassified and poor-confidence mavericks and placing them in a Current Maverick Set. Presenting the Current Maverick Set to an analyst to determine (1) if the maverick should be deferred and placed in the Deferred Maverick Set; or (2) if the maverick is assigned to a certain group it is removed from the Current Maverick Set and placed in the Firmly Assigned Set; or (3) if the input set of software objects should have certain features added to, or removed from them, or (4) if the similarity function coefficient estimation should be returned to the estimation procedure wherein this time, its inputs are: the original set of software objects less the members of the Deferred Maverick Set and the Current Maverick Set plus the members of the Firmly Assigned Set; the weights of the features and the coefficients previously used, which may be modified if need be; and the modified group assignments. Updated values for the coefficients are received, and when maverick resolution is complete, the reoptimizing stops.
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