An ANTLR-based Feature Extraction and Detection System for Scratch

Abstract

Scratch, a visual programming language used by youth, has received widespread attention of education field. Quality Hound is an effective tool to detect the features of Scratch. However, its detection rules are not sufficiently complete, which incurs incomprehensive results. In this paper, we propose an ANTLR-based feature extraction and detection system to solve this problem. Specifically, nine novel programming feature detection rules are abstracted and applied in our model. The experimental results show our system can effectively extract programming features from projects and provide feedback for students and teachers.