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Chapter 7. Implications
The key argument in our study is that when dealing with skewed distributions,
it is hard to derive sensible conclusions from descriptive
statistical summary measures such as mean and median. Hence, we
recommend that software metric tool developers should present the distribution
histogram (relative and cumulative), the Lorenz curve and the
Gini Coefficient values. When this information is shown along with the
typical range for the Gini Coefficient values, the tool users can reason
about the nature of their software more effectively. Furthermore,
rather than remove the widely used summary measures (such as mean
and standard deviation) the tools should display a warning about the
limitations of these measures. This approach will serve to inform and
educate the users allowing them to interpret the data better.
The second recommendation based on our study is related to the feature
allowing users to set thresholds for metrics. The common approach that
is currently provided allows users to identify abstractions that breach
a threshold value. Tool vendors suggest that this feature is helpful
in identifying complex classes or methods. However, our observations
show that it is normal in software systems to have a small proportion of
classes and methods that are significantly more complex than others.
We also found that this typical skewed nature does not impact on the
evolution as many systems in our data set were able to sustain change
and growth over many years. Based on these findings, we recommend
that thresholds should be based on relative proportions rather than absolute
values. For example, the tools should allow users to set 10% of
the methods can have a cyclomatic complexity greater than 7. This approach
also has the advantage of educating the users about the nature
of the data that is being measured. Where sufficient historical data is
available, the range for acceptable proportional threshold values can be
determined from previous successful projects.
7.2.3 Testing and Monitoring Changes
Software testing literature recommends that developers should target
complex classes when testing object oriented software [29], as the complexity
tends to increase defect density [205]. Our findings provide addi-
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