  Change impact analysis consists in predicting the impact of a code change in
a software application. In this paper, we take a learning perspective on change
impact analysis and consider the problem formulated as follows. The artifacts
that are considered are methods of object-oriented software, the change under
study is a change in the code of the method, the impact is the test methods
that fail because of the change that has been performed. We propose an
algorithm, called LCIP that learns from past impacts to predict future impacts.
To evaluate our system, we consider 7 Java software applications totaling
214,000+ lines of code. We simulate 17574 changes and their actual impact
through code mutations, as done in mutation testing. We find that LCIP can
predict the impact with a precision of 69%, a recall of 79%, corresponding to a
F-Score of 55%.
