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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsRepair
