MACER: A modular framework for accelerated compilation error repair

Abstract

Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has pedagogical applications for novice programmers who find compiler error messages cryptic and unhelpful. Existing works frequently involve black-box application of generative models, e.g. sequence-to-sequence prediction (TRACER) or reinforcement learning (RLAssist). Although convenient, this approach is inefficient at targeting specific error types as well as increases training costs. We present MACER, a novel technique for accelerated error repair based on a modular segregation of the repair process into repair identification and repair application. MACER uses powerful yet inexpensive learning techniques such as multi-label classifiers and rankers to first identify the type of repair required and then apply the suggested repair. Experiments indicate that this fine-grained approach offers not only superior error correction, but also much faster training and prediction. On a benchmark dataset of 4K buggy programs collected from actual student submissions, MACER outperforms existing methods by 20% at suggesting fixes for popular errors while being competitive or better at other errors. MACER offers a training time speedup of 2 × over TRACER and 800 × over RLAssist, and a test time speedup of 2 - 4 × over both.