Automatically Improving The Code Quality Of Rust Via LLM
Author(s)
Cheng, Xiang
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Abstract
In this thesis, the research objective is to define and resolve the challenges of leveraging LLM
to automatically improve Rust’s code quality. The application of LLMs to Rust code quality
improvement requires addressing fundamental challenges in three key areas: generating
compilable code that satisfies Rust’s strict type system, detecting subtle safety violations that
escape traditional analysis, and creating comprehensive test suites that achieve meaningful
code coverage. These challenges necessitate novel approaches that combine LLMs with
program analysis techniques specifically designed for Rust’s unique characteristics.
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Date
2025-07-29
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Text
Resource Subtype
Dissertation