  Code metrics are easy to define, but not so easy to justify. It is hard to
prove that a metric is valid, i.e., that measured numerical values imply
anything on the vaguely defined, yet crucial software properties such as
complexity and maintainability. This paper employs statistical analysis and
tests to check some "believable" presumptions on the behavior of software and
metrics measured for this software. Among those are the reliability presumption
implicit in the application of any code metric, and the presumption that the
magnitude of change in a software artifact is correlated with changes to its
version number.
  Putting a suite of 36 metrics to the trial, we confirm most of the
presumptions. Unexpectedly, we show that a substantial portion of the
reliability of some metrics can be observed even in random changes to
architecture. Another surprising result is that Boolean-valued metrics tend to
flip their values more often in minor software version increments than in major
increments.
