Advances in Automated Pedagogical Compile-time Error Repair

Abstract

Automated pedagogical error repair (APER) is the task of suggesting fixes to buggy programs written by beginner or novice programmers. APER tools have been shown to greatly improve the learning experience for students for whom error messages offered by compilers or runtime environments are either unhelpful and often misleading. Consequently, several APER tools have been proposed in literature using a variety of powerful machine learning techniques including sequence-to-sequence modelling (TRACER), reinforcement learning (RLAssist), graph attention (DrRepair) and decision trees (MACER). Despite offering high repair rates, these tools are often bulky, requiring several days of training and extensive GPU resources. This paper describes CAPER, a novel APER tool for the C programming language that offers 4-5% higher repair accuracy than existing APER tools on multiple benchmark error repair datasets. CAPER has the added advantage of being significantly lighter and faster than most existing tools, being able to train on a CPU and yet offering training speedups of 2 × over TRACER, 600 × over RLAssist and 700 × over DrRepair. The paper also describes PyPER, an extension of CAPER to the Python programming language. Code for CAPER is available at https://github.com/purushottamkar/caper/