Learning to Boost the Efficiency of Modern Code Review

TL;DR

This paper proposes AI techniques based on graph learning to improve the efficiency of Modern Code Review by learning standards from existing projects, aiming to reduce review time while maintaining quality.

Contribution

It introduces a novel approach of applying graph-learning algorithms to MCR, enabling AI to assist or partially replace human reviewers by learning from online repositories.

Findings

Graph-learning models can effectively learn coding standards.

AI assistance reduces review time without compromising quality.

Use of online repositories enables scalable training data collection.

Abstract

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.