The goal of Software Change Impact Analysis is to identify artifacts
(typically source-code files or individual methods therein) potentially
affected by a change.  Recently, there has been increased interest
in mining software change impact based on evolutionary coupling.
A particularly promising approach uses association rule mining to
uncover potentially affected artifacts from patterns in the system's
change history.  Two main considerations when using this approach
are the history length, the number of transactions from the change
history used to identify the impact of a change, and history age,
the number of transactions that have occurred since patterns were
last mined from the history.  Although history length and age can
significantly affect the quality of mining results, few guidelines
exist on how to best select appropriate values for these two
parameters.

In this paper, we empirically investigate the effects of history
length and age on the quality of change impact analysis using mined
evolutionary coupling.  Specifically, we report on a series of
systematic experiments using three state-of-the-art mining algorithms
that involve the change histories of two large industrial systems
and 17 large open source systems.  In these experiments, we vary
the length and age of the history used to mine software change
impact, and assess how this affects precision and applicability.
Results from the study are used to derive practical guidelines for
choosing history length and age when applying association rule
mining to conduct software change impact analysis.