Difference between revisions of "Measurement"

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Starting from the bottom of the following figure, the two attributes are DCPs and requirements. Assume some sample project developed 223 DCPs in models for 21 requirements. Dividing the number of DCPs by the number of requirements produces a derived measure of 10.6 DCPs per requirement (i.e., DCP density). Compare this derived data to historical data, where the density was 16.3 DCPs per requirement. A possible interpretation of this information product is that the requirement traceability accuracy for the current project is better than the organizational average. If the current DCP density has a variance greater than 10 from the organizational average, then it may be necessary for the requirement engineers to attend a training class on techniques for developing better requirements.
 
Starting from the bottom of the following figure, the two attributes are DCPs and requirements. Assume some sample project developed 223 DCPs in models for 21 requirements. Dividing the number of DCPs by the number of requirements produces a derived measure of 10.6 DCPs per requirement (i.e., DCP density). Compare this derived data to historical data, where the density was 16.3 DCPs per requirement. A possible interpretation of this information product is that the requirement traceability accuracy for the current project is better than the organizational average. If the current DCP density has a variance greater than 10 from the organizational average, then it may be necessary for the requirement engineers to attend a training class on techniques for developing better requirements.
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[[Image:Product_measurement_example.jpg|center|Measurement Example]]

Latest revision as of 14:40, 14 February 2007