  Developers spend 70% of their time understanding code. Code that is easy to
read can save time, while hard-to-read code can lead to the introduction of
bugs. However, it is difficult to establish what makes code more
understandable. Although there are guides and directives on improving code
understandability, in some contexts, these practices can have a detrimental
effect. Practical software development projects often employ code review to
improve code quality, including understandability. Reviewers are often senior
developers who have contributed extensively to projects and have an in-depth
understanding of the impacts of different solutions on code understandability.
This paper is an early research proposal to recommend code understandability
improvements based on code reviewer knowledge. The core of the proposal
comprises a dataset of code understandability improvements extracted from code
reviews. This dataset will serve as a basis to train machine learning systems
to recommend understandability improvements.
