Rust Programming Research Articles

Expressive rule-based modeling and fast simulation for dynamic compartments.

Compartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes. Integrating compartmental dynamics is challenging for the design of the modeling language and the simulation engine. The language should support a concise yet flexible modeling of compartmental dynamics. Our work is based on ML-Rules, a rule-based language for multi-level cell biological modeling that supports a wide variety of compartmental dynamics, whose syntax we slightly adapt. To develop an efficient simulation engine for compartmental dynamics, we combine specific data structures and new and existing algorithms and implement them in the Rust programming language. We evaluate the concept and implementation using two case studies from existing cell-biological models. The execution of these models outperforms previous simulations of ML-Rules by two orders of magnitude. Finally, we present a prototype of a WebAssembly-based implementation to allow for a low barrier of entry when exploring the language and associated models without the need for local installation.

Rustlantis: Randomized Differential Testing of the Rust Compiler

Compilers are at the core of all computer architecture. Their middle-end and back-end are full of subtle code that is easy to get wrong. At the same time, the consequences of compiler bugs can be severe. Therefore, it is important that we develop techniques to increase our confidence in compiler correctness, and to help find the bugs that inevitably happen. One promising such technique that has successfully found many compiler bugs in the past is randomized differential testing , a fuzzing approach whereby the same program is executed with different compilers or different compiler settings to detect any unexpected differences in behavior. We present Rustlantis , the first fuzzer for the Rust programming language that is able to find new correctness bugs in the official Rust compiler. To avoid having to deal with Rust’s strict type and borrow checker, Rustlantis directly generates MIR, the central IR of the Rust compiler for optimizations. The program generation strategy of Rustlantis is a combination of statically tracking the state of the program, obscuring the program state for the compiler, and decoy blocks to lead optimizations astray. This has allowed us to identify 22 previously unknown bugs in the Rust compiler, most of which have been fixed.

Sound Borrow-Checking for Rust via Symbolic Semantics

The Rust programming language continues to rise in popularity, and as such, warrants the close attention of the programming languages community. In this work, we present a new foundational contribution towards the theoretical understanding of Rust’s semantics. We prove that LLBC, a high-level, borrow-centric model previously proposed for Rust’s semantics and execution, is sound with regards to a low-level pointer-based language à la CompCert. Specifically, we prove the following: that LLBC is a correct view over a traditional model of execution; that LLBC’s symbolic semantics are a correct abstraction of LLBC programs; and that LLBC’s symbolic semantics act as a borrow-checker for LLBC, i.e. that symbolically-checked LLBC programs do not get stuck when executed on a heap-and-addresses model of execution. To prove these results, we introduce a new proof style that considerably simplifies our proofs of simulation, which relies on a notion of hybrid states. Equipped with this reasoning framework, we show that a new addition to LLBC’s symbolic semantics, namely a join operation, preserves the abstraction and borrow-checking properties. This in turn allows us to add support for loops to the Aeneas framework; we show, using a series of examples and case studies, that this unlocks new expressive power for Aeneas.

A Contextual Model for Visual Information Processing

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning is finding a set of transformation rules (context) and applying them to the incoming information, producing an interpretation. Then, the interpretation is compared to something already seen and is stored in memory. Information can have different meanings in different contexts. A mathematical model of a context processor and a differential contextual space which can perform the interpretation is discussed and developed in this paper. This study examines whether the basic principles of differential contextual spaces work in practice. The model is developed with Rust programming language and trained on black and white images which are rotated and shifted both horizontally and vertically according to the saccades and torsion movements of a human eye. Then, a picture that has never been seen in the particular transformation, but has been seen in another one, is exposed to the model. The model considers the image in all known contexts and extracts the meaning. The results show that the program can successfully process black and white images which are transformed by shifts and rotations. This research prepares the grounding for further investigations of the contextual model principles with which general intelligence might operate.

RefinedRust: A Type System for High-Assurance Verification of Rust Programs

RefinedRust: A Type System for High-Assurance Verification of Rust Programs

Bigtools: a high-performance BigWig and BigBed library in Rust.

The BigWig and BigBed file formats were originally designed for the visualization of next-generation sequencing data through a genome browser. Due to their versatility, these formats have long since become ubiquitous for the storage of processed sequencing data and regularly serve as the basis for downstream data analysis. As the number and size of sequencing experiments continues to accelerate, there is an increasing demand to efficiently generate and query BigWig and BigBed files in a scalable and robust manner, and to efficiently integrate these functionalities into data analysis environments and third-party applications. Here, we present Bigtools, a feature-complete, high-performance, and integrable software library for generating and querying both BigWig and BigBed files. Bigtools is written in the Rust programming language and includes a flexible suite of command line tools as well as bindings to Python. Bigtools is cross-platform and released under the MIT license. It is distributed on Crates.io, Bioconda, and the Python Package Index, and the source code is available at https://github.com/jackh726/bigtools.

Functional Ownership through Fractional Uniqueness

Ownership and borrowing systems, designed to enforce safe memory management without the need for garbage collection, have been brought to the fore by the Rust programming language. Rust also aims to bring some guarantees offered by functional programming into the realm of performant systems code, but the type system is largely separate from the ownership model, with type and borrow checking happening in separate compilation phases. Recent models such as RustBelt and Oxide aim to formalise Rust in depth, but there is less focus on integrating the basic ideas into more traditional type systems. An approach designed to expose an essential core for ownership and borrowing would open the door for functional languages to borrow concepts found in Rust and other ownership frameworks, so that more programmers can enjoy their benefits. One strategy for managing memory in a functional setting is through uniqueness types, but these offer a coarse-grained view: either a value has exactly one reference, and can be mutated safely, or it cannot, since other references may exist. Recent work demonstrates that linear and uniqueness types can be combined in a single system to offer restrictions on program behaviour and guarantees about memory usage. We develop this connection further, showing that just as graded type systems like those of Granule and Idris generalise linearity, a Rust-like ownership model arises as a graded generalisation of uniqueness. We combine fractional permissions with grading to give the first account of ownership and borrowing that smoothly integrates into a standard type system alongside linearity and graded types, and extend Granule accordingly with these ideas.

Cocoon: Static Information Flow Control in Rust

Information flow control (IFC) provides confidentiality by enforcing noninterference, which ensures that high-secrecy values cannot affect low-secrecy values. Prior work introduces fine-grained IFC approaches that modify the programming language and use non-standard compilation tools, impose run-time overhead, or report false secrecy leaks—all of which hinder adoption. This paper presents Cocoon, a Rust library for static type-based IFC that uses the unmodified Rust language and compiler. The key insight of Cocoon lies in leveraging Rust’s type system and procedural macros to establish an effect system that enforces noninterference. A performance evaluation shows that using Cocoon increases compile time but has no impact on application performance. To demonstrate Cocoon’s utility, we retrofitted two popular Rust programs, the Spotify TUI client and Mozilla’s Servo browser engine, to use Cocoon to enforce limited confidentiality policies

Profiling Programming Language Learning

This paper documents a year-long experiment to “profile” the process of learning a programming language: gathering data to understand what makes a language hard to learn, and using that data to improve the learning process. We added interactive quizzes to The Rust Programming Language, the official textbook for learning Rust. Over 13 months, 62,526 readers answered questions 1,140,202 times. First, we analyze the trajectories of readers. We find that many readers drop-out of the book early when faced with difficult language concepts like Rust’s ownership types. Second, we use classical test theory and item response theory to analyze the characteristics of quiz questions. We find that better questions are more conceptual in nature, such as asking why a program does not compile vs. whether a program compiles. Third, we performed 12 interventions into the book to help readers with difficult questions. We find that on average, interventions improved quiz scores on the targeted questions by +20

SEGUL: Ultrafast, memory-efficient and mobile-friendly software for manipulating and summarizing phylogenomic datasets.

Phylogenetic studies now routinely require manipulating and summarizing thousands of data files. For most of these tasks, currently available software requires considerable computing resources and substantial knowledge of command-line applications. We develop an ultrafast and memory-efficient software, SEGUL, that performs common phylogenomic dataset manipulations and calculates statistics summarizing essential data features. Our software is available as standalone command-line interface (CLI) and graphical user interface (GUI) applications, and as a library for Rust, R and Python, with possible support of other languages. The CLI and library versions run native on Windows, Linux and macOS, including Apple ARM Macs. The GUI version extends support to include mobile iOS, iPadOS and Android operating systems. SEGUL leverages the high performance of the Rust programming language to offer fast execution times and low memory footprints regardless of dataset size and platform choice. The inclusion of a GUI minimizes bioinformatics barriers to phylogenomics while SEGUL's efficiency reduces economic barriers by allowing analysis on inexpensive hardware. Our support for mobile operating systems further enables teaching phylogenomics where access to computing power is limited.

SafeNet: Towards mitigating replaceable unsafe Rust code via a recommendation‐based approach

AbstractRust is a system‐level programming language with advantages in memory safety. It ensures that any Rust programs without unsafe code should not incur undefined behaviours. However, unsafe code still plays an essential role in Rust to achieve low‐level control. Therefore, a major design pattern of Rust programs is interior unsafe, which wraps unsafe code as safe APIs and handles all undefined behaviours internally. Rust standard library already provides a rich set of safe APIs to facilitate Rust code development. Nevertheless, due to unfamiliarity with these APIs, developers may misuse unnecessary unsafe code and suffer memory‐safety risks. In this paper, we investigate an approach to mitigate replaceable unsafe code. We first analyse unsafe APIs of the Rust standard library and summarize their common usage patterns. Each pattern corresponds to one or several code samples in our knowledge base. Then, we develop an approach to automatically recognize the usage pattern and recommend corresponding code samples. Our approach leverages dataflow analysis to exclude impossible patterns and employs a BERT‐based machine learning model to find the most similar pattern among the rest. We have conducted evaluation experiments with 472 unsafe code snippets collected from GitHub projects and successfully recognized the pattern of 394 snippets. We hope our approach can assist developers in detecting unnecessary unsafe code and suggesting safe alternatives.

Profiling with trust: system monitoring from trusted execution environments

AbstractLarge-scale attacks on IoT and edge computing devices pose a significant threat. As a prominent example, Mirai is an IoT botnet with 600,000 infected devices around the globe, capable of conducting effective and targeted DDoS attacks on (critical) infrastructure. Driven by the substantial impacts of attacks, manufacturers and system integrators propose Trusted Execution Environments (TEEs) that have gained significant importance recently. TEEs offer an execution environment to run small portions of code isolated from the rest of the system, even if the operating system is compromised. In this publication, we examine TEEs in the context of system monitoring and introduce the Trusted Monitor (TM), a novel anomaly detection system that runs within a TEE. The TM continuously profiles the system using hardware performance counters and utilizes an application-specific machine-learning model for anomaly detection. In our evaluation, we demonstrate that the TM accurately classifies 86% of 183 tested workloads, with an overhead of less than 2%. Notably, we show that a real-world kernel-level rootkit has observable effects on performance counters, allowing the TM to detect it. Major parts of the TM are implemented in the Rust programming language, eliminating common security-critical programming errors.

Blockchain-Powered Education: A Sustainable Approach for Secured and Connected University Systems

The collection and examination of student data, encompassing academic achievements, awards, and certifications, assume an essential function within the field of education as a means of showing students’ capabilities. Nevertheless, it is crucial to note that regular paper-based records are vulnerable to both physical destruction and the act of fabrication, while standard databases can have security holes. Moreover, the process of manually gathering physical papers from centralized organizations is both laborious and complicated. To address the concerns above and foster sustainability in the field of education, this study first suggests using Scorechain. This innovative solution integrates blockchain technology into a comprehensive data-management system for managing all student-related data. Secondly, by utilizing the inherent security features of blockchain technology, Scorechain develops a stable multi-role hierarchy, increasing the integrity and reliability of data. This also facilitates the efficient transfer of information among various stakeholders, including parents, recruiters, and educational institutions, thus fostering transparency and accountability. Lastly, the Scorechain system facilitates collaboration and data exchange among universities inside a shared network. Scorechain was constructed using the Rust programming language and is based on the Substrate blockchain architecture. It underwent careful development, testing, and analysis to ensure operational efficiency. The feasibility and long-term viability of Scorechain in genuine educational contexts are highlighted as blockchain technology facilitates seamless integration into the education sector.

SafeDrop: Detecting Memory Deallocation Bugs of Rust Programs via Static Data-flow Analysis

Rust is an emerging programming language that aims to prevent memory-safety bugs. However, the current design of Rust also brings side effects, which may increase the risk of memory-safety issues. In particular, it employs ownership-based resource management and enforces automatic deallocation of unused resources without using the garbage collector. It may therefore falsely deallocate reclaimed memory and lead to use-after-free or double-free issues. In this article, we study the problem of invalid memory deallocation and propose SafeDrop , a static path-sensitive data-flow analysis approach to detect such bugs. Our approach analyzes each function of a Rust crate iteratively in a flow-sensitive and field-sensitive way. It leverages a modified Tarjan algorithm to achieve scalable path-sensitive analysis and a cache-based strategy for efficient inter-procedural analysis. We have implemented our approach and integrated it into the Rust compiler. Experiment results show that the approach can successfully detect all such bugs in our experiments with a limited number of false positives and incurs a very small overhead compared to the original compilation time.

Memory Errors and Memory Safety: A Look at Java and Rust

We consider memory errors and memory safety in the cases of the Java and Rust programming languages. We also give a view of how type safety fits in.

Verus: Verifying Rust Programs using Linear Ghost Types

The Rust programming language provides a powerful type system that checks linearity and borrowing, allowing code to safely manipulate memory without garbage collection and making Rust ideal for developing low-level, high-assurance systems. For such systems, formal verification can be useful to prove functional correctness properties beyond type safety. This paper presents Verus, an SMT-based tool for formally verifying Rust programs. With Verus, programmers express proofs and specifications using the Rust language, allowing proofs to take advantage of Rust's linear types and borrow checking. We show how this allows proofs to manipulate linearly typed permissions that let Rust code safely manipulate memory, pointers, and concurrent resources. Verus organizes proofs and specifications using a novel mode system that distinguishes specifications, which are not checked for linearity and borrowing, from executable code and proofs, which are checked for linearity and borrowing. We formalize Verus' linearity, borrowing, and modes in a small lambda calculus, for which we prove type safety and termination of specifications and proofs. We demonstrate Verus on a series of examples, including pointer-manipulating code (an xor-based doubly linked list), code with interior mutability, and concurrent code.

Aliasing Limits on Translating C to Safe Rust

The Rust language was created to provide safe low-level systems programming. There is both industrial and academic interest in the problem of (semi-)automatically translating C code to Rust in order to exploit Rust's safety guarantees. We study the effectiveness and limitations of existing techniques for automatically translating unsafe raw pointers (in Rust programs translated from C) into safe Rust references via ownership and lifetime inference. Our novel evaluation methodology enables our study to extend beyond prior studies, and to discover new information contradicting the conclusions of prior studies. We find that existing translation methods are severely limited by a lack of precision in the Rust compiler's safety checker, causing many safe pointer manipulations to be labeled as potentially unsafe. Leveraging this information, we propose methods for improving translation, based on encoding the results of a more precise analysis in a manner that is understandable to an unmodified Rust compiler. We implement one of our proposed methods, increasing the number of pointers that can be translated to safe Rust references by 75% over the baseline (from 12% to 21% of all pointers).

Hardware/Software Co-Assurance for the Rust Programming Language Applied to Zero Trust Architecture Development

Zero Trust Architecture requirements are of increasing importance in critical systems development. Zero trust tenets hold that no implicit trust be granted to assets based on their physical or network location. Zero Trust development focuses on authentication, authorization, and shrinking implicit trust zones to the most granular level possible, while maintaining availability and minimizing authentication latency. Performant, high-assurance cryptographic primitives are thus central to successfully realizing a Zero Trust Architecture. The Rust programming language has garnered significant interest and use as a modern, type-safe, memory-safe, and potentially formally analyzable programming language. Our interest in Rust particularly stems from its potential as a hardware/software co-assurance language for developing Zero Trust Architectures. We describe a novel environment enabling Rust to be used as a High-Level Synthesis (HLS) language, suitable for secure and performant Zero Trust application development. Many incumbent HLS languages are a subset of C, and inherit many of the well-known security shortcomings of that language. A Rust-based HLS brings a single modern, type-safe, memory-safe, high-assurance development language for both hardware and software. To study the benefits of this approach, we crafted a Rust HLS subset, and developed a frontend to the hardware/software co-assurance toolchain due to Russinoff and colleagues at Arm, used primarily for floating-point hardware formal verification. This allows us to leverage a number of existing hardware/software co-assurance tools with a minimum investment of time and effort. In this paper, we describe our Rust subset, detail our prototype toolchain, and describe the implementation, performance analysis, formal verification and validation of representative Zero Trust algorithms and data structures written in Rust, emphasizing cryptographic primitives and common data structures.

FeOs: An Open-Source Framework for Equations of State and Classical Density Functional Theory

In this work, we present an open-source software package, referred to as FeOs─Framework for Equations of State and Classical Density Functional Theory. FeOs is a collection of interfaces and data types that can be used (1) to implement thermodynamic equations of state and Helmholtz energy functionals for classical density functional theory, and (2) to compute thermodynamic properties of pure substances and mixtures, phase equilibria, and interfacial properties such as surface tensions and adsorption isotherms. The framework is written in the Rust programming language with a complete Python interface and is designed with a focus on usability and extensibility. It is openly available on GitHub (https://github.com/feos-org/feos). Equations of state can be implemented in Rust, yielding performant code, or as a Python class, which is useful for prototyping and with less emphasis on execution speed. In both cases, the user has to implement a single function: the Helmholtz energy. FeOs then uses generalized (hyper-) dual numbers to evaluate the Helmholtz energy as well as the required exact partial (higher-order) derivatives. Using this type of automatic differentiation delivers performance without the need for implementing any analytical derivatives. The performance is further enhanced by a caching mechanism that avoids duplicate model evaluations. Together with the core interfaces and functionalities for equations of state and classical density functional theory, we provide implementations for multiple models such as the PC-SAFT equation of state (with homo- and heterosegmented group contribution methods) and Helmholtz energy functionals (including segment-based functionals). To showcase a selection of FeOs’ features, an example study of the adsorption of biogas in porous media using the PC-SAFT functional is provided.

Higher-Order Leak and Deadlock Free Locks

Reasoning about concurrent programs is challenging, especially if data is shared among threads. Program correctness can be violated by the presence of data races—whose prevention has been a topic of concern both in research and in practice. The Rust programming language is a prime example, putting the slogan fearless concurrency in practice by not only employing an ownership-based type system for memory management, but also using its type system to enforce mutual exclusion on shared data. Locking, unfortunately, not only comes at the price of deadlocks but shared access to data may also cause memory leaks . This paper develops a theory of deadlock and leak freedom for higher-order locks in a shared memory concurrent setting. Higher-order locks allow sharing not only of basic values but also of other locks and channels, and are themselves first-class citizens. The theory is based on the notion of a sharing topology , administrating who is permitted to access shared data at what point in the program. The paper first develops higher-order locks for acyclic sharing topologies, instantiated in a λ-calculus with higher-order locks and message-passing concurrency. The paper then extends the calculus to support circular dependencies with dynamic lock orders, which we illustrate with a dynamic version of Dijkstra’s dining philosophers problem. Well-typed programs in the resulting calculi are shown to be free of deadlocks and memory leaks, with proofs mechanized in the Coq proof assistant.