7,482 papers · page 27 of 375
We present a new approach to scaling exact inference for probabilistic programs, using generating functions (GFs) as a compilation target. Existing methods that target representations like binary decision diagrams (BDDs) achieve strong state-of-the-art results. We show that a com…
We present Coneris, the first *higher-order concurrent separation logic* for reasoning about error probability bounds of higher-order concurrent probabilistic programs with higher-order state. To support modular reasoning about concurrent (non-probabilistic) program modules, stat…
Approximations play a pivotal role in verifying deep neural networks (DNNs). Existing approaches typically rely on either single-neuron approximations (simpler to design but less precise) or multi-neuron approximations (higher precision but significantly more complex to construct…
In static analysis frameworks for Java, the bytecode frontend serves as a critical component, transforming complex, stack-based Java bytecode into a more analyzable register-based, typed 3-address code representation. This transformation often significantly influences the overall…
In this paper, we present structural abstraction refinement, a novel framework for verifying the threshold problem of probabilistic programs. Our approach represents the structure of a Probabilistic Control-Flow Automaton (PCFA) as a Markov Decision Process (MDP) by abstracting a…
Tokenization (also known as scanning or lexing) is a computational task that has applications in the lexical analysis of programs during compilation and in data extraction and analysis for unstructured or semi-structured data (e.g., data represented using the JSON and CSV data fo…
Domain-specific languages (DSLs) for machine learning are revolutionizing the speed and efficiency of machine learning workloads as they enable users easy access to high-performance compiler optimizations and accelerators. However, to take advantage of these capabilities, a user …
Large Language Models (LLMs) have emerged as a promising alternative to traditional static program analysis methods, such as symbolic execution, offering the ability to reason over code directly without relying on theorem provers or SMT solvers. However, LLMs are also inherently …
We study the implementability problem for an expressive class of symbolic communication protocols involving multiple participants. Our symbolic protocols describe infinite states and data values using dependent refinement predicates. Implementability asks whether a global protoco…
Large code models (LCMs), pre-trained on vast code corpora, have demonstrated remarkable performance across a wide array of code-related tasks. Supervised fine-tuning (SFT) plays a vital role in aligning these models with specific requirements and enhancing their performance in p…
We introduce Stencil-Lifting, a novel system for automatically converting stencil kernels written in low-level languages within legacy code into semantically equivalent Domain-Specific Language (DSL) implementations. Targeting the efficiency bottlenecks of existing verified lifti…
We propose a neural-symbolic style of program analysis that systematically incorporates informal information in a Datalog program analysis. The analysis is converted into a probabilistic analysis by attaching probabilities to its rules. And its output becomes a ranking of possibl…
The advances in modern storage technologies necessitate the development of new input/output (I/O) APIs to maximize their performance benefits. However, migrating existing software to use different APIs poses significant challenges due to mismatches in computational models and com…
Rust has become a popular system programming language that strikes a balance between memory safety and performance. Rust’s type system ensures the safety of low-level memory controls; however, a well-typed Rust program is not guaranteed to enjoy high performance. This article stu…
Database-backed applications form the backbone of modern software, yet their complexity poses significant challenges for static analysis. These applications involve intricate interactions among application code, diverse database frameworks such as JDBC, Hibernate, and Spring Data…
Imprecise probability is concerned with uncertainty about which probability distributions to use. It has applications in robust statistics and machine learning. We look at programming language models for imprecise probability. Our desiderata are that we would like our model to su…
Conflict-free replicated data types (CRDTs) are distributed data structures designed for fault tolerance and high availability. CRDTs have historically been taxonomized into state-based CRDTs, in which replicas apply updates locally and periodically broadcast their state to other…
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 op…
The Dependent Calculus of Indistinguishability (DCOI) uses dependency tracking to identify irrelevant arguments and uses indistinguishability during type conversion to enable proof irrelevance, supporting run-time and compile-time irrelevance with the same uniform mechanism. DCOI…
Given the high cost of formal verification, a large system may include differently analyzed components: a few are fully verified, and the rest are tested. Currently, there is no reasoning system that can soundly compose these heterogeneous analyses and derive the overall formal g…