  Continuous Integration (CI) has become a well-established software
development practice for automatically and continuously integrating code
changes during software development. An increasing number of Machine Learning
(ML) based approaches for automation of CI phases are being reported in the
literature. It is timely and relevant to provide a Systemization of Knowledge
(SoK) of ML-based approaches for CI phases. This paper reports an SoK of
different aspects of the use of ML for CI. Our systematic analysis also
highlights the deficiencies of the existing ML-based solutions that can be
improved for advancing the state-of-the-art.
