Proceedings

scale are constant. Nevertheless, most of the research that found variable returns to
scale was developed using heterogeneous datasets, which may originate a better
fitting of nonlinear functions. So, in the present research we tested the accuracy of
two models derived from the Cobb-Douglas production function against a linear
model. We use data from a single organization, which guarantees a better fulfillment
of the ceteris paribus assumption that should be present in the estimation of
econometric models.
Our main results indicate that a linear model with constant returns to scale provides
more accurate estimations than the Cobb-Douglas-derived cost functions. In addition,
we found that Cobb-Douglas models are not stable in the presence of outliers. This
result has practical implications. The most important is that we should be cautious
when using models derived from a dataset containing projects from organizations
which are very different. Software production process heavily depends on immaterial
assets such as engineers abilities and the efficiency of the organizational structure.
These factors are mainly firm-specific.
Another result of our research is that the influence of software development capital on
software costs is very little in our models. This means that either software
development capital is not a relevant cost driver or the proxy for its measurement is
not an accurate one. This is an interesting avenue for future research.
Finally, we must also mention others directions for future research. First, additional
datasets can be obtained and analyzed in order to gain further evidence of the results
of the present paper. Second, and due to the existence of outliers, robust regression
procedures could improve estimation results. In this regard, and having into account
the presence of heteroskedaticity, a quantile regression approach could yield a better
understanding of the behavior of software effort costs.
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