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Developers spend 70% of their time understanding code. Code that is easy toread can save time, while hard-to-read code can lead to the introduction ofbugs. However, it is difficult to establish what makes code moreunderstandable. Although there are guides and directives on improving codeunderstandability, in some contexts, these practices can have a detrimentaleffect. Practical software development projects often employ code review toimprove code quality, including understandability. Reviewers are often seniordevelopers who have contributed extensively to projects and have an in-depthunderstanding of the impacts of different solutions on code understandability.This paper is an early research proposal to recommend code understandabilityimprovements based on code reviewer knowledge. The core of the proposalcomprises a dataset of code understandability improvements extracted from codereviews. This dataset will serve as a basis to train machine learning systemsto recommend understandability improvements.

Recommending Code Understandability Improvements based on Code Reviews