An Empirical Study of Bugs in the rustc Compiler
This program is tentative and subject to change.
Rust is gaining popularity for its well-known memory safety guarantees and high performance, distinguishing it from C/C++ and JVM-based languages. Its compiler, rustc, enforces these guarantees through specialized mechanisms such as trait solving, borrow checking, and specific optimizations. However, Rust’s unique language mechanisms introduce complexity to its compiler, resulting in bugs that are uncommon in traditional compilers. With Rust’s increasing adoption in safety-critical domains, understanding these language mechanisms and their impact on compiler bugs is essential for improving the reliability of both rustc and Rust programs. Such understanding could provide the foundation for developing more effective testing strategies tailored to rustc. Improving the quality of rustc testing is essential for enhancing compiler reliability, which in turn strengthens the safety and correctness of all Rust programs, as compiler bugs can silently propagate into every compiled program. Yet, we still lack a large-scale, detailed, and in-depth study of rustc bugs.
To bridge this gap, this work presents a comprehensive and systematic study of rustc bugs, specifically those originating in semantic analysis and intermediate representation (IR) processing, which are stages that implement essential Rust language features such as ownership and lifetimes. Our analysis examines issues and fixes reported between 2022 and 2024, with a manual review of 301 valid issues. We categorize these bugs based on their causes, symptoms, affected compilation stages, and test case characteristics. Additionally, we evaluate existing rustc testing tools to assess their effectiveness and limitations. Our key findings include: (1) rustc bugs primarily arise from Rust’s type system and lifetime model, with frequent errors in the High-Level Intermediate Representation (HIR) and Mid-Level Intermediate Representation (MIR) modules due to complex checkers and optimizations; (2) bug-revealing test cases often involve unstable features, advanced trait usages, lifetime annotations, standard APIs, and specific optimization levels; (3) while both valid and invalid programs can trigger bugs, existing testing tools struggle to detect non-crash errors, underscoring the need for further advancements in rustc testing.
This program is tentative and subject to change.
Thu 16 OctDisplayed time zone: Perth change
16:00 - 17:30 | |||
16:00 15mTalk | An Empirical Study of Bugs in the rustc Compiler OOPSLA Zixi Liu Nanjing University, Yang Feng Nanjing University, Yunbo Ni The Chinese University of Hong Kong, Shaohua Li The Chinese University of Hong Kong, Xizhe Yin Nanjing University, Qingkai Shi Nanjing University, Baowen Xu Nanjing University, Zhendong Su ETH Zurich | ||
16:15 15mTalk | DESIL: Detecting Silent Bugs in MLIR Compiler Infrastructure OOPSLA Chenyao Suo Tianjin University, Jianrong Wang Tianjin University, Yongjia Wang College of Intelligence and Computing, Tianjin University, Jiajun Jiang Tianjin University, Qingchao Shen Tianjin University, Junjie Chen Tianjin University | ||
16:30 15mTalk | GALA: A High Performance Graph Neural Network Acceleration LAnguage and Compiler OOPSLA Damitha Lenadora University of Illinois at Urbana-Champaign, Nikhil Jayakumar University of Texas at Austin, Chamika Sudusinghe University of Illinois at Urbana-Champaign, Charith Mendis University of Illinois at Urbana-Champaign | ||
16:45 15mTalk | Non-interference Preserving Optimising Compilation OOPSLA Julian Rosemann Saarland University, Saarland Informatics Campus, Sebastian Hack Saarland University, Saarland Informatics Campus, Deepak Garg MPI-SWS | ||
17:00 15mTalk | Synchronized Behavior Checking: A Method for Finding Missed Compiler Optimizations OOPSLA Yi Zhang Nanjing University, Yu Wang Nanjing University, Linzhang Wang Nanjing University, Ke Wang Peking University | ||
17:15 15mTalk | Tabby: A Synthesis-Aided Compiler for High-Performance Zero-Knowledge Proof Circuits OOPSLA Junrui Liu University of California, Santa Barbara, Jiaxin Song University of Illinois at Urbana-Champaign, Yanning Chen University of Toronto, Hanzhi Liu University of California, Santa Barbara & Riema Labs, Hongbo Wen University of California, Santa Barbara & Riema Labs, Luke Pearson Polychain Capital, Yanju Chen University of California, San Diego, Yu Feng University of California at Santa Barbara | ||