  Modern Code Review (MCR) is the process of reviewing new code changes that
need to be merged with an existing codebase. As a developer, one may receive
many code review requests every day, i.e., the review requests need to be
prioritised. Manually prioritising review requests is a challenging and
time-consuming process. To address the above problem, we conducted an
industrial case study at Ericsson aiming at developing a tool called Pineapple,
which uses a Bayesian Network to prioritise code review requests. To validate
our approach/tool, we deployed it in a live software development project at
Ericsson, wherein more than 150 developers develop a telecommunication product.
We focused on evaluating the predictive performance, feasibility, and
usefulness of our approach. The results indicate that Pineapple has competent
predictive performance (RMSE = 0.21 and MAE = 0.15). Furthermore, around 82.6%
of Pineapple's users believe the tool can support code review request
prioritisation by providing reliable results, and around 56.5% of the users
believe it helps reducing code review lead time. As future work, we plan to
evaluate Pineapple's predictive performance, usefulness, and feasibility
through a longitudinal investigation.
