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Chapter 5. Growth Dynamics
LIC –
SIC 0.93 –
NOB 0.80 0.81 –
IDC 0.18 0.24 0.15 –
ODC 0.86 0.82 0.77 0.08 –
NOM 0.71 0.66 0.44 0.26 0.63 –
PMC 0.21 0.20 0.09 0.27 0.18 0.75 –
NOA 0.70 0.79 0.48 0.23 0.50 0.53 0.16 –
FOC 0.96 0.87 0.80 0.09 0.85 0.62 0.16 0.67 –
TCC 0.78 0.78 0.53 0.11 0.68 0.56 0.16 0.82 0.84 –
Metric LIC SIC NOB IDC ODC NOM PMC NOA FOC TCC
Table 5.2: Spearman’s Rank Correlation Coefficient values for one
version (0.3.0) of JasperReports. Strong correlation values are highlighted.
5.4 Observations
In this section we present our observations within the context of the
key research questions. The findings are summarised in Section 5.4.8
and discussed in Section 5.5.
5.4.1 Correlation between measures
As an initial step in our analysis to identify if a subset of the measures
selected could be eliminated, we ran a correlation analysis between
the 10 metrics under investigation (see Table 5.1). The range of
the observed correlation coefficient values is summarized (graphically)
in Figure 5.4 using both a box-plot as well as a histogram. The figure
shows, for each metric, the distribution of the correlation coefficient
against the other 9 metrics.
Our observations are summarised as follows:
• There exists a strong positive correlation (i.e., > 0.8 [265]) between
some different measures consistently across our entire data set.
The high correlation coefficient values can be seen in skewed histograms
chart in Figure 5.4 for most metrics. Across all system,
except for In-Degree Count and Public Method Count the correlation
coefficient values are in general very high.
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