This paper proposes using large language models (LLMs) to generate and compositionally verify software implementations directly from natural language specifications, showing promising preliminary resu…

This paper presents Forge, a pipeline that uses formal verification to ensure the safety and certification of LLM-generated Java code.

AutoSOUP is a system that automates component-level memory-safety verification by generating Safety-Oriented Unit Proofs, leveraging a hybrid LLM-based architecture to overcome manual workflow limitat…

The paper introduces ProofLoop, a novel ReAct agent that uses a solver-in-the-loop approach to automatically generate and formally verify SystemVerilog Assertions (SVA) from natural language specifica…

Proposed method, KBSpec, uses external and internal knowledge to improve formal specification generation by LLMs, increasing verification pass rates and producing more high-completeness specifications…

This paper formalizes the task of generating Domain-Specific Language (DSL) code from natural language descriptions and introduces the PolkitBench dataset for evaluating LLM-based DSL code generation.

AutoPass is a multi-agent framework for compiler performance tuning using a large language model, enabling it to query compiler-internal optimization states and analyze the intermediate representation…

This paper proposes an automated static verification method for a subset of WebAssembly using a CHCs satisfiability solver, addressing challenges of handling indirect function calls and analyzing larg…

The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reli…

SAILOR automates the construction of symbolic execution harnesses by combining static analysis and LLM-based synthesis, significantly improving the scalability and effectiveness of vulnerability disco…

The paper introduces TRAILS~, a novel method that improves code correctness validation by grounding LLM reasoning in concrete (input, output) pairs derived from specifications, achieving state-of-the-…

The paper introduces SecPI, a fine-tuning pipeline that teaches reasoning language models (RLMs) to autonomously internalize structured security reasoning, significantly improving secure code generati…

This study formally verified 3,500 AI-generated code artifacts and found that a majority (55.8%) contain exploitable security vulnerabilities, regardless of the LLM used.

KVerus is a retrieval-augmented system that significantly improves the scalability and resilience of formal verification for Rust code by managing complex cross-module dependencies and adapting to cod…

VeriCWEty proposes an embedding-based framework to detect and classify common software vulnerabilities (CWEs) in Verilog RTL code at both module and line levels, achieving high detection accuracy.

The paper proposes the Communicating Finite Automata (CFA) abstraction and a framework for component program refactoring in large language model inference services, achieving significant speedup and r…

The paper introduces codebadger, a Model Context Protocol (MCP) server that integrates Joern's Code Property Graph (CPG) with LLMs, enabling large language models to perform large-scale, semantic prog…

Assertain is an automated framework that uses large language models and design analysis to generate high-quality, executable security assertions for hardware designs, significantly outperforming state…

The paper proposes TRAVEL, a framework for automated C-to-Rust translation using Monte Carlo Tree Search and reinforcement learning, improving accuracy and success rate.