Proceedings

The Desharnais dataset contains eleven attributes. Three of these variables are suitable
to the estimation of our models. First, as a measure of the software output we
considered the adjusted functional points. The function-point method was proposed by
Albrecht (1979) and is based on the functionality of the software rather than on its
size in terms of lines of code. Most of the prior research efforts use functional points
as the proxy for size. More recently, extensions of this methodology have been
developed, as for example the object-point method (Sneed, 1996).
Second, as proxy for the software development capital we chose the developers team
experience, measured in years. As for some of the cases the value of this variable was
equal to zero, we added 0.1 to all the observations in order to allow the log
transformation. In our view, this attribute is a better proxy of the software
development capital than others which are often used such as team size (see, e.g.,
Pendharkar et al., 2008). It is not clear that a bigger team size leads to higher
productivity because bigger teams may involve increased communication
requirements, increased conflicts and increased project overhead activities (Banker
and Kemerer, 1989).
Finally, with regard to the the project effort, its measurement can be considered a
challenging endeavor. The Project Management Institute (2008) defines the project
effort metric as “the number of labor units required to complete an activity or other
project element. This is usually expressed as staff hours, staff days, or staff weeks and
should not be confused with duration”. So we measured project effort using the
amount of person-hours needed to the completion of the project, as Desharnais dataset
does not include the total cost in monetary units. A limitation of this, which is shared
with the majority of the prior research efforts, is that our models do not take into
account that the experience of the developers has an effect on the costs of the project.
Wages of developers with more experience are generally higher.
Table 1 contains some descriptive information on the studied variables.
Table 1. Descriptive information
Statistics Y (Software effort) x (team experience in z (adjusted functional
years)
points)
Mean 4833.909 2.298 298.013
Standard Deviation 4188.185 1.328 182.263
Minimum 546 0 73
Maximum 23940 4 1127
Percentile 10 1093.4 1 116.8
Percentile 25 2306.5 1 171
Percentile 50 3542 2 258
Percentile 75 5848.5 4 379.5
Percentile 90 10733.8 4 523
4. RESULTS
Table 2 shows the estimation results. We provide parameter estimates and the
accuracy measures explained above. It is noticeable that LL-CD and linear models
show significant levels of heteroskedasticity, so robust standard errors are provided.
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