  Context. Modern Code Review (MCR) is being adopted in both open source and
commercial projects as a common practice. MCR is a widely acknowledged quality
assurance practice that allows early detection of defects as well as poor
coding practices. It also brings several other benefits such as knowledge
sharing, team awareness, and collaboration.
  Problem. In practice, code reviews can experience significant delays to be
completed due to various socio-technical factors which can affect the project
quality and cost. For a successful review process, peer reviewers should
perform their review tasks in a timely manner while providing relevant feedback
about the code change being reviewed. However, there is a lack of tool support
to help developers estimating the time required to complete a code review prior
to accepting or declining a review request.
  Objective. Our objective is to build and validate an effective approach to
predict the code review completion time in the context of MCR and help
developers better manage and prioritize their code review tasks.
  Method. We formulate the prediction of the code review completion time as a
learning problem. In particular, we propose a framework based on regression
models to (i) effectively estimate the code review completion time, and (ii)
understand the main factors influencing code review completion time.
