Soteria: Efficient Symbolic Execution as a Functional Library
This addresses the problem of inefficiency and complexity in symbolic execution for developers, offering a simpler and more effective approach, though it is incremental in improving existing methods.
The paper tackles the trade-offs in symbolic execution tools by introducing Soteria, a functional library for building engines directly in source languages, resulting in competitive or superior performance and bug detection compared to state-of-the-art tools like Kani and CBMC.
Symbolic execution (SE) tools often rely on intermediate languages (ILs) to support multiple programming languages, promising reusability and efficiency. In practice, this approach introduces trade-offs between performance, accuracy, and language feature support. We argue that building SE engines \emph{directly} for each source language is both simpler and more effective. We present Soteria, a lightweight OCaml library for writing SE engines in a functional style, without compromising on performance, accuracy or feature support. Soteria enables developers to construct SE engines that operate directly over source-language semantics, offering \emph{configurability}, compositional reasoning, and ease of implementation. Using Soteria, we develop Soteria$^{\text{Rust}}$, the \emph{first} Rust SE engine supporting Tree Borrows (the intricate aliasing model of Rust), and Soteria$^{\text{C}}$, a compositional SE engine for C. Both tools are competitive with or outperform state-of-the-art tools such as Kani, Pulse, CBMC and Gillian-C in performance and the number of bugs detected. We formalise the theoretical foundations of Soteria and prove its soundness, demonstrating that sound, efficient, accurate, and expressive SE can be achieved without the compromises of ILs.