  Modern Code Review (MCR) is a standard in all kinds of organizations that
develop software. MCR pays for itself through perceived and proven benefits in
quality assurance and knowledge transfer. However, the time invest in MCR is
generally substantial. The goal of this thesis is to boost the efficiency of
MCR by developing AI techniques that can partially replace or assist human
reviewers. The envisioned techniques distinguish from existing MCR-related AI
models in that we interpret these challenges as graph-learning problems. This
should allow us to use state-of-science algorithms from that domain to learn
coding and reviewing standards directly from existing projects. The required
training data will be mined from online repositories and the experiments will
be designed to use standard, quantitative evaluation metrics. This research
proposal defines the motivation, research-questions, and solution components
for the thesis, and gives an overview of the relevant related work.
