Your English writing platform
Discover LudwigExact(1)
Table 16 IQV rate for each cluster Clustering All systems Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 WMC 0.910 0.869 0.941 0.957 0.889 0.672 LOC 0.877 0.943 0.912 0.895 0.768 CBO 0.842 0.946 0.952 0.806 0.762 Table 17 Descriptive statistics of the unit test case metrics (Univariate clustering - LOC) LOC Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Nb.
Similar(59)
In consideration of the pertinent reports of the LOC, a direct correspondence of our areas FG1 and FG2 to a LOC-cluster seems to be unlikely, as they are located between both classical clusters, ventral to LO and posterior to pFs.
Indeed, the center of gravity of FG2 is in close proximity to the coordinates of the "branching point" between both LOC-clusters described by Malach et al. (1995).
Figure 4 Univariate clustering – LOC: mean and coefficient of variation distribution.
As stated in the previous section, the unit test case metrics are less volatile in the clustering based on the WMC and LOC metrics compared to the clustering based on CBO (Figure 7).
It can also be seen from Table 14 that the distribution of the internal software class attribute metrics reflects properly the classification of tested classes: the mean values of the metrics LOC, WMC and CBO increase from cluster 1 to cluster 5 (most complex classes).
They included a region in left, mid-fusiform gyrus (Fig. 2 b ), as well as a large cluster extending from the LOC to the posterior middle temporal gyrus (Table 1).
Table 14 gives the descriptive statistics of the three internal software class attribute metrics LOC, WMC and CBO corresponding to the five clusters (1 5).
For Houses (blue) large clusters were found in the right LOC, left anterior superior temporal sulcus and the right medial orbitofrontal region.
Tables 17, 18 and 19 give the descriptive statistics of the unit test case metrics corresponding to the five clusters obtained respectively for the variables size (LOC), complexity (WMC) and coupling (CBO).
The first univariate clustering we performed was based on the variable size (LOC).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com