MACER: A Modular Framework for Accelerated Compilation Error Repair

TL;DR

MACER is a modular framework that significantly improves the speed and accuracy of automated compilation error repair by using discriminative learning to identify and apply specific fixes, outperforming existing methods.

Contribution

MACER introduces a modular, discriminative learning-based approach for faster and more accurate compilation error repair, surpassing existing black-box methods in efficiency and effectiveness.

Findings

Outperforms existing methods by 20% in fix accuracy.

Achieves 2x training speedup over TRACER.

Achieves 800x training speedup over RLAssist.

Abstract

Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has generated significant interest in recent years. Apart from being a tool of general convenience, automated code repair has significant pedagogical applications for novice programmers who find compiler error messages cryptic and unhelpful. Existing approaches largely solve this problem using a blackbox-application of a heavy-duty generative learning technique, such as sequence-to-sequence prediction (TRACER) or reinforcement learning (RLAssist). Although convenient, such black-box application of learning techniques makes existing approaches bulky in terms of training time, as well as inefficient at targeting specific error types. 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 discriminative 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 the fine-grained approach adopted by MACER 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 that exactly match the fix desired by the student. MACER is also competitive or better than existing methods at all error types -- whether popular or rare. MACER offers a training time speedup of 2x over TRACER and 800x over RLAssist, and a test time speedup of 2-4x over both.