Custom neural architectures designed for reference resolution in code can dramatically improve both robustness and scalability compared to generic sequence-to-sequence models, with practical benefits for code analysis tasks.
This paper tackles the problem of resolving references in program code by framing it as a sequence-to-sequence task. The authors create synthetic benchmarks for reference rewriting and propose new neural architectures that significantly outperform standard models, handling sequences 10x longer than baselines.