An empirical investigation of changes in some software properties over time

Software 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 plausible assumptions on the behavior of software and metrics measured for this software in retrospective on its versions evolution history. Among those are the reliability assumption implicit in the application of any code metric, and the assumption that the magnitude of change, i.e., increase or decrease of its size, 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 assumptions on a large repository of software artifacts. Surprisingly, 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.