Improving Readability of Scratch Programs with Search-based Refactoring

TL;DR

This paper presents a search-based method to automatically refactor Scratch programs, improving their readability by applying atomic transformations that reduce complexity and entropy, aiding learners and teachers.

Contribution

It introduces a novel multi-objective search approach for automated refactoring of Scratch code, addressing the lack of abstraction mechanisms in block-based programming.

Findings

Refactorings reduced complexity and entropy in 70.4% of cases

354 projects improved in at least one metric without regressions

Automated transformations help novices and teachers improve code quality

Abstract

Block-based programming languages like Scratch have become increasingly popular as introductory languages for novices. These languages are intended to be used with a "tinkering" approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, Scratch lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve Scratch code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of Scratch programs. By automating these transformations it is possible to explore the space of possible variations of Scratch programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given Scratch program and therefore form refactorings. Evaluation on a random sample of 1000 Scratch programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4% of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code.