Tootfinder

NL in the Middle: Code Translation with LLMs and Intermediate Representations
Chi-en Amy Tai, Pengyu Nie, Lukasz Golab, Alexander Wong
https://arxiv.org/abs/2507.08627

NL in the Middle: Code Translation with LLMs and Intermediate Representations
Studies show that large language models (LLMs) produce buggy code translations. One avenue to improve translation accuracy is through intermediate representations, which could provide structured insights to guide the model's understanding. We explore whether code translation using LLMs can benefit from intermediate representations via natural language (NL) and abstract syntax trees (ASTs). Since prompt engineering greatly affects LLM performance, we consider several ways to integrate these repr…

Auf die letzte Ausgabe der @… hatte ich relativ ausführlich reagiert:
https://spov.kddk.eu/show/https://podcasts.social/@fre…

AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
Zixian Zhang, Takfarinas Saber
https://arxiv.org/abs/2506.14470

AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
As one of the most detrimental code smells, code clones significantly increase software maintenance costs and heighten vulnerability risks, making their detection a critical challenge in software engineering. Abstract Syntax Trees (ASTs) dominate deep learning-based code clone detection due to their precise syntactic structure representation, but they inherently lack semantic depth. Recent studies address this by enriching AST-based representations with semantic graphs, such as Control Flow Gra…