  In general, estimating software effort using a Use Case Point (UCP) size
requires the use of productivity as a second prediction factor. However, there
are three drawbacks to this approach: (1) there is no clear procedure for
predicting productivity in the early stages, (2) the use of fixed or limited
productivity ratios does not allow research to reflect the realities of the
software industry, and (3) productivity from historical data is often
challenging. The new UCP datasets now available allow us to perform further
empirical investigations of the productivity variable in order to estimate the
UCP effort. Accordingly, four different prediction models based on productivity
were used. The results showed that learning productivity from historical data
is more efficient than using classical approaches that rely on default or
limited productivity values. In addition, predicting productivity from
historical environmental factors is not often accurate. From here we conclude
that productivity is an effective factor for estimating the software effort
based on the UCP in the presence and absence of previous historical data.
Moreover, productivity measurement should be flexible and adjustable when
historical data is available
