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Another indicator which is often calculated is Pred(25), which is the percentage of
projects for which MRE is less than 25% (see, e.g., Shepperd and Schofield, 1997,
and Vinay Kumar et al., 2008):
1 n
Pr ed(
25)
= × ∑ ( MRE ≤ 0,
25)
(10)
n 1=
i
The last measure is the average square root error index (ASREI):
N
[ ( ) ] Yˆ
= ∑ i − i Y
1
ASREI (10)
N
i=
1
Where N is the number of projects in the sample. ASREI is a robust indicator which is
commonly used to evaluate goodness of fit on data (see, e.g., Hale et al., 2000; Smith
et al., 2001; Pendharkar et al., 2008).
Since LL-CD model predicts log effort, for the calculation of the accuracy measures
corresponding to this model we take the anti-log values to compute the value of
model-predicted effort. This procedure is commonly used in studies that consider loglinear
models (see, e.g., Pendharkar, 2008).
The four measures are calculated both using the original sample and a jackknife
procedure. Jackknife is a validation technique consisting in removing each case and
then using the rest of the cases to predict the removed one. This method has been used
by some of the prior researchers (e.g., Shepperd and Schofield, 1997). The reason is to
detect the overfitting problems that nonlinear/nonparametrical models often suffer.
3. SAMPLE AND VARIABLES
In this research we used the Desharnais dataset, which comprises 81 commercial
projects developed by a Canadian software house in the late 80s. This dataset was
initially compiled by Desharnais (1989) and is currently available at Sayyad Shirabad
and Menzies (2005). It has been extensively used by researchers on software effort
costs (Shepperd and Schofield, 1997; Burgess and Lefley, 2001; Oliveira et al., 2010;
Azzeh et al., 2011, among many others).
Despite the projects in the Desharnais database are more than 20 years old it is
noticeable that it is still used in scientific research. One of its advantages is that all the
projects were developed by the same software firm and the time span is relatively
short. This leads to a better assessment of the models, as it guarantees the ceteris
paribus requirement which must be fulfilled in the estimation of econometric models.
That is, the variability of the sample with regard to organizational and technological
factors which are not explicitly represented by variables in the dataset is lesser than in
other datasets which comprise projects from several houses / several decades.
Projects n. 38, 44, 66 and 75 of the Desharnais dataset have missing data. As well as
many of the previous researchers that used this dataset, we discarded these projects.
So, our final sample comprises the 77 cases with complete information.
~ 686 ~