Type:	Monografia
Title:	Geração de código rust aleatório utilizando modelos grandes de linguagem para testes de compiladores
Author:	Magnabosco, Fernando Schreiner
First advisor:	Feitosa, Samuel da Silva
Resume:	This work proposes an approach for testing Rust compilers using Large Language Models (LLMs) to generate random code focused on unstable features. The methodology implements a differential testing system that uses the official rustc compiler as an oracle to filter semantically valid test cases before submitting them to gccrs, an alternative compiler under development. The system targets complex features such as trait specialization and constant evalua- tion. Results from 1,403 generated instances show that while LLMs struggle with the strict semantic rules of Rust’s nightly features (yielding a 3.7% validity rate), the filtering process proved crucial. The validated test cases revealed that the target compiler (gccrs) is currently limited by infrastructure bottlenecks—specifically due to missing language items and standard library linkage—rather than logic errors in complex passes. Crucially, the absence of critical crashes (ICEs) suggests stability in the compiler’s frontend parsing, even if backend integration remains incomplete. This study demons- trates that LLM-based fuzzing can serve as an effective "maturity probe"for emerging compilers.
Keywords:	Montadores e compiladores Testes Geração de código Inteligência artificial
Language:	por
Country:	Brasil
Publisher:	Universidade Federal da Fronteira Sul
Acronym of the institution:	UFFS
College, Institute or Department:	Campus Chapecó
Type of Access:	Acesso Aberto
URI:	https://rd.uffs.edu.br/handle/prefix/9187
Issue Date:	2025
Appears in Collections:	Ciência da Computação

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