Code Quality Improvement for All: Automated Refactoring for Scratch - Techapalokul, Peeratham; Tilevich, Eli (Proceedings (IEEE Symposium on Visual Languages and Human-Centric Computing), 2019)
Block-based programming has been overwhelmingly successful in revitalizing introductory computing education and in facilitating end-user development. However, poor code quality makes block-based programs hard to understand, modify, and reuse, thus hurting the educational and productivity effectiveness of blocks. There is great potential benefit in empowering programmers in this domain to systematically improve the code quality of their projects. Refactoring--improving code quality while preserving its semantics--has been widely adopted in traditional software development. In this work, we introduce refactoring to Scratch. We define four new Scratch refactorings: Extract Custom Block, Extract Parent Sprite, Extract Constant, and Reduce Variable Scope. To automate the application of these refactorings, we enhance the Scratch programming environment with powerful program analysis and transformation routines. To evaluate the utility of these refactorings, we apply them to remove the code smells detected in a representative dataset of 448 Scratch projects. We also conduct a between-subjects user study with 24 participants to assess how our refactoring tools impact programmers. Our results show that refactoring improves the subjects' code quality metrics, while our refactoring tools help motivate programmers to improve code quality.