26,098 papers · page 32 of 1,305
We present RSFuzz, a new technique to enhance grammar-based fuzzing by reducing the generation of coverage-equivalent inputs during testing. Grammar-based fuzzers apply production rules from a given grammar (e.g., forming a derivation tree) to generate well-structured inputs for …
The rapid proliferation and increasing complexity of software demand robust quality assurance, with graphical user interface (GUI) testing playing a pivotal role. Crowdsourced testing has proven effective in this context by leveraging the diversity of human testers to achieve ric…
Intel Software Guard Extensions (SGX) provides a trusted execution environment (TEE) for applications to protect runtime code and data from the untrusted environment. All code residing in the enclave, including LibOSes and the libraries, is all taken into the Trusted Computing Ba…
Large Language Models (LLMs) are capable of generating syntactically correct and functionally complete programs, greatly streamlining software development. However, recent studies reveal that these programs typically execute substantially slower than human-optimized counterparts.…
Smart contracts automate the management of high-value assets, where vulnerabilities can lead to catastrophic financial losses. In the task of automated smart contract generation using Large Language Models (LLMs), this challenge is amplified by two interconnected failures: first,…
Bash script comprehension is a significant challenge in Linux environments due to Bash's syntactic freedom and complex command structures. Despite its critical role in system administration and development, Bash scripts often lack adequate comments, hindering code readability and…
Software interacts with hardware through Instruction Set Architectures (ISAs), such as x86, ARM, and RISC-V. Although many developers may be unaware of ISA heterogeneity, ISA-specific code is pervasive in foundational software systems that underpin the digital infrastructure of h…
Code translation transforms code between programming languages while preserving functionality, which is critical in software development and maintenance. While traditional learning-based code translation methods have limited effectiveness due to the lack of sufficient parallel tr…
Safety alignment aims to prevent Large Language Models (LLMs) from producing harmful content. However, safety alignment remains vulnerable to malicious instructions. Red teaming is a critical methodology for identifying such vulnerabilities in LLMs. Existing approaches often rely…
Translating C code into safe Rust is an effective way to ensure memory safety. Compared to rule-based approaches, which often produce largely unsafe Rust code, LLM-based methods generate more idiomatic and safer Rust by leveraging extensive training on human-written code. Despite…
Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. However, insecure logging practices can inadvertently expose sensitive information or enable attacks such as log injection, posing ser…
The rapid proliferation of diverse programming languages presents both opportunities and challenges for developing multilingual code LLMs. While existing techniques often train code LLMs by simply aggregating multilingual code data, few explore the deeper relationships between pr…
Automated Code Review (ACR) is crucial for software quality, yet existing benchmarks often fail to reflect real-world complexities, hindering the evaluation of modern Large Language Models (LLMs). Current benchmarks frequently focus on fine-grained code units, lack complete proje…
Large language models (LLMs) are increasingly used for tasks that implicitly reduce to Boolean satisfiability (SAT), yet their reasoning ability on SAT remains unclear. We present a systematic study of LLMs on 2-SAT and 3-SAT, together with two canonical reductions—Vertex Cover a…
With the increasing complexity and scale of integrated circuit (IC) designs, automated circuit design methods are essential for Verilog implementation. Although Large Language Models (LLMs) perform well in general-purpose coding such as C++ and Python, their performance in Verilo…
Smart contracts have attracted rapid development and widespread application. Due to the complexity of real-world smart contracts, it is error-prone to correctly enforce all intended functionalities in code implementations, resulting in unintended functional behaviors and security…
Interactive theorem proving (ITP) is a powerful approach to ensuring the correctness of complex software systems. However, it often requires substantial manual effort, which makes it costly to use in practice. Recently, neural network based approaches have shown promise in automa…
The quality of foundational libraries is critical to the reliability of modern software ecosystems. However, developers often do not have enough time to test for various reasons. Recent studies show that 68% of deep learning libraries lack unit tests and their absence negatively …
Coding standards are essential for maintaining consistent and high-quality code across teams and projects. Linters help developers enforce these standards by detecting code violations. However, manual linter configuration is complex and expertise-intensive, and the diversity and …
Scripting languages like Python, Ruby, or PHP are integral to modern software development. Despite security measures like memory safety and sandboxing, vulnerabilities within these engines can lead to critical issues such as remote code execution or sandbox escapes. A particularl…