As the complexity of software systems grows, it becomes increasingly
difficult for developers to be aware of all the dependencies that
exist between artifacts (e.g., files or methods) of a system.  Change
recommendation has been proposed as a technique to overcome this
problem, as it suggests to a developer relevant source-code artifacts
related to her changes.  Association rule mining has shown promise
to deriving such recommendations by uncovering relevant patterns
in the system's change history.  The strength of the mined association
rules is captured using a variety of interestingness measures.
However, state-of-the-art recommendation engines typically use only
the rule with the highest interestingness value when more than one
rule applies.  In contrast, we argue that when multiple rules apply,
this indicates collective evidence, and aggregating those rules
(and their evidence) will lead to more accurate change recommendation.

To investigate this hypothesis we conduct a large empirical study
of 15 open source software systems and two systems from our industry
partners.  We evaluate association rule aggregation using four
variants of the change history for each system studied, enabling
us to compare two different levels of granularity in two different
scenarios.  Furthermore, we study 40 interestingness measures using
the rules produced by two different mining algorithms.  The results
show that (1) between 13% and 90% of change recommendations can be
improved by rule aggregation, (2) rule aggregation almost always
improves change recommendation for both algorithms and all measures,
and (3) fine-grained histories benefit more from rule aggregation.