Rust is a memory-safe programming language that significantly improves software security. Existing codebases written in unsafe memory languages, such as C, must first be transpiled to Rust to take advantage of Rust’s improved safety guarantees. RustAssure presents a system that uses Large Language Models (LLMs) to automatically transpile existing C code-bases to Rust. RustAssure uses prompt engineering techniques to maximize the chances of the LLM generating idiomatic and safe Rust code. Moreover, because LLMs often generate code with subtle bugs that can be missed under traditional unit or fuzz testing, RustAssure performs differential symbolic testing to establish the semantic similarity between the original C and LLM-transpiled Rust code. We evaluated RustAssure with five real-world applications and libraries, and showed that our system is able to generate compilable Rust functions for 89.8% of all C functions, of which 72% produced equivalent symbolic return values for both the C and Rust functions.

The dynamical properties of many complex physical and biological systems can be quantified from the energy landscape theory. Previous approaches focused on estimating the transition rate from landscape reconstruction based on data. However, for general non-equilibrium systems (such as gene regulatory systems), both the energy landscape and the probability flux are important to determine the transition rate between attractors. In this work, we proposed a data-driven approach to estimate non-equilibrium transition rate, which combines the kernel density estimation and non-equilibrium transition rate theory. Our approach shows superior performance in estimating transition rate from data, compared with previous methods, due to the introduction of a nonparametric density estimation method and the new saddle point by considering the effects of flux. We demonstrate the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.