# MACER: A Modular Framework for Accelerated Compilation Error Repair

arXiv: https://arxiv.org/abs/2005.14015 · HTML: https://mlsanity.com/paper/2005.14015/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 2 of 2)

- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments](https://mlsanity.com/paper/2404.01754/) (2024)

## Abstract (arXiv)

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.