  Iterative impact analysis (IIA) is a process that allows developers to
estimate the impacted units of a software change. Starting from a single
impacted unit, the developers inspect its interacting units via program
dependencies to identify the ones that are also impacted, and this process
continues iteratively. Experience has shown that developers often miss impacted
units and inspect many irrelevant units. In this work, we study propagation
heuristics that guide developers to find the actual impacted units and
termination heuristics that help to decide whether the estimated impact is
complete. The roles of these two kinds of heuristics are complementary and
affect both the precision and recall when used during IIA. We investigated
several propagation heuristics adapted from previously published papers and
combined them with a practical termination heuristic. We developed a
reenactment process that simulates the actions of developers who use those
heuristics during IIA, and we assessed their performance. The software changes
for our reenactment were mined from the repositories of open source projects.
We found that IIA provides better recall than the other known impact analysis
techniques. However the IIA with the propagation heuristics that we
investigated does not supersede IIA combined with a random inspection, and
hence these heuristics do not help the IIA.
