Automated program repair using neural models has shown promising results on benchmark datasets, yet practical deployment remains limited. In this study, we examine whether a small transformer model can meaningfully repair real-world Java bugs and whether syntactic correctness is a reliable proxy for semantic correctness. We fine-tune CodeT5-small (60.5M parameters) on 52,364 Java bug-fix pairs from CodeXGLUE and evaluate both token-level performance and syntactic validity using AST parsing. While the model converges cleanly and achieves high grammatical correctness, producing syntactically valid Java code in approximately ninety-four percent of cases, it fails to generate correct repairs under exact-match evaluation, achieving zero exact matches. In approximately eighty percent of cases, the model reproduces the buggy input verbatim.
@article{arxiv.2512.22216,
title = {Syntax Is Not Enough: An Empirical Study of Small Transformer Models for Neural Code Repair},
author = {Shaunak Samant},
journal= {arXiv preprint arXiv:2512.22216},
year = {2025}
}