Neural architectures for resolving references in program code
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908966959710208 |
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| author | Szalay, Gergő Kovács, Gergely Zsolt Teleki, Sándor Pintér, Balázs Gregorics, Tibor |
| author_facet | Szalay, Gergő Kovács, Gergely Zsolt Teleki, Sándor Pintér, Balázs Gregorics, Tibor |
| contents | Resolving and rewriting references is fundamental in programming languages. Motivated by a real-world decompilation task, we abstract reference rewriting into the problems of direct and indirect indexing by permutation. We create synthetic benchmarks for these tasks and show that well-known sequence-to-sequence machine learning architectures are struggling on these benchmarks. We introduce new sequence-to-sequence architectures for both problems. Our measurements show that our architectures outperform the baselines in both robustness and scalability: our models can handle examples that are ten times longer compared to the best baseline. We measure the impact of our architecture in the real-world task of decompiling switch statements, which has an indexing subtask. According to our measurements, the extended model decreases the error rate by 42%. Multiple ablation studies show that all components of our architectures are essential. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14073 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural architectures for resolving references in program code Szalay, Gergő Kovács, Gergely Zsolt Teleki, Sándor Pintér, Balázs Gregorics, Tibor Machine Learning Neural and Evolutionary Computing Resolving and rewriting references is fundamental in programming languages. Motivated by a real-world decompilation task, we abstract reference rewriting into the problems of direct and indirect indexing by permutation. We create synthetic benchmarks for these tasks and show that well-known sequence-to-sequence machine learning architectures are struggling on these benchmarks. We introduce new sequence-to-sequence architectures for both problems. Our measurements show that our architectures outperform the baselines in both robustness and scalability: our models can handle examples that are ten times longer compared to the best baseline. We measure the impact of our architecture in the real-world task of decompiling switch statements, which has an indexing subtask. According to our measurements, the extended model decreases the error rate by 42%. Multiple ablation studies show that all components of our architectures are essential. |
| title | Neural architectures for resolving references in program code |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2604.14073 |