7.3 Use Computation to Convert Counts to Estimates

If you collect historical data related to counts, you can convert the counts to something useful, such as estimated effort. Table 7-1 lists examples of quantities you might count and the data you would need to compute an estimate from the count.

Table 7-1: Examples of Quantities That Can Be Counted for Estimation Purposes
Quantity to Count	Historical Data Needed to Convert the Count to an Estimate
Marketing requirements	Average effort hours per requirement for development Average effort hours per requirement for independent testing Average effort hours per requirement for documentation Average effort hours per requirement to create engineering requirements from marketing requirements
Features	Average effort hours per feature for development and/or testing
Use cases	Average total effort hours per use case Average number of use cases that can be delivered in a particular amount of calendar time
Stories	Average total effort hours per story Average number of stories that can be delivered in a particular amount of calendar time
Engineering requirements	Average number of engineering requirements that can be formally inspected per hour Average effort hours per requirement for development/test/documentation
Function Points	Average development/test/documentation effort per Function Point Average lines of code in the target language per Function Point
Change requests	Average development/test/documentation effort per change request (depending on variability of the change requests, the data might be decomposed into average effort per small, medium, and large change request)
Web pages	Average effort per Web page for user interface work Average whole-project effort per Web page (less reliable, but can be an interesting data point)
Reports	Average effort per report for report work
Dialog boxes	Average effort per dialog for user interface work
Database tables	Average effort per table for database work Average whole-project effort per table (less reliable, but can be an interesting data point)
Classes	Average effort hours per class for development Average effort hours to formally inspect a class Average effort hours per class for testing
Defects found	Average effort hours per defect to fix Average effort hours per defect to regression test Average number of defects that can be corrected in a particular amount of calendar time
Configuration settings	Average effort per configuration setting
Lines of code already written	Average number of defects per line of code Average lines of code that can be formally inspected per hour Average new lines of code from one release to the next
Test cases already written	Average amount of release-stage effort per test case

Tip #32

Collect historical data that allows you to compute an estimate from a count.

Example of counting defects late in a project Once you have the kind of data described in the table, you can use that data as a more solid basis for creating estimates than expert judgment. If you know that you have 400 open defects, and you know that the 250 defects you've fixed so far have averaged 2 hours per defect, you know that you have about 400 x 2 equals 800 hours of work to fix the open defects.

Example of estimation by counting Web pages If your data says that so far your project has taken an average of 40 hours to design, code, and test each Web page with dynamic content, and you have 12 Web pages left, you know that you have something like 12 x 40 equals 480 hours of work left on the remaining Web pages.

The important point in these examples is that there is no judgment in these estimates. You count, and then you compute. This process helps keep the estimates free from bias that would otherwise degrade their accuracy. For counts that you already have available—such as number of defects—such estimates also require very low effort.

Tip #33

Don't discount the power of simple, coarse estimation models such as average effort per defect, average effort per Web page, average effort per story, and average effort per use case.