  Version Control Systems (VCS) are frequently used to support development of
large-scale software projects. A typical VCS repository of a large project can
contain various intertwined branches consisting of a large number of commits.
If some kind of unwanted behaviour (e.g. a bug in the code) is found in the
project, it is desirable to find the commit that introduced it. Such commit is
called a regression point. There are two main issues regarding the regression
points. First, detecting whether the project after a certain commit is correct
can be very expensive as it may include large-scale testing and/or some other
forms of verification. It is thus desirable to minimise the number of such
queries. Second, there can be several regression points preceding the actual
commit; perhaps a bug was introduced in a certain commit, inadvertently fixed
several commits later, and then reintroduced in a yet later commit. In order to
fix the actual commit it is usually desirable to find the latest regression
point.
  The currently used distributed VCS contain methods for regression
identification, see e.g. the git bisect tool. In this paper, we present a new
regression identification algorithm that outperforms the current tools by
decreasing the number of validity queries. At the same time, our algorithm
tends to find the latest regression points which is a feature that is missing
in the state-of-the-art algorithms. The paper provides an experimental
evaluation of the proposed algorithm and compares it to the state-of-the-art
tool git bisect on a real data set.
