Recent advancements in large language models (LLMs) have shown very impressive capabilities in code generation across many programming languages. However, even ...
In complex systems with many compute nodes containing multiple CPUs that are coherent within each node, a key challenge is maintaining efficient and correct coh...
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by l...
This paper proposes a technique to help choose the best formal specification candidate among a set of alternatives. Given a set of specifications, our technique...
A well-known testing method for the safety evaluation and real-time validation of automotive software systems (ASSs) is Fault Injection (FI). In accordance with...
A suitable choice of the representation of candidate solutions is crucial for the efficiency of evolutionary algorithms and related metaheuristics. We focus on ...
Recent advancements in artificial intelligence (AI) and its widespread integration into mobile software applications have received significant attention, highli...
Linux kernel evolution breaks drivers through API/ABI changes, semantic shifts, and security-hardening updates. We introduce DRIVEBENCH, an executable corpus of...
Deep Learning (DL) compilers have been widely utilized to optimize DL models for efficient deployment across various hardware. Due to their vital role in the DL...
Intrinsic functions are specialized functions provided by the compiler that efficiently operate on architecture-specific hardware, allowing programmers to write...
We present a novel framework that integrates Large Language Models (LLMs) into the Git bisect process for semantic fault localization. Traditional bisect assume...
Large Language Models (LLMs) are increasingly integrated into code editors to provide AI-powered code suggestions. Yet many of these suggestions are ignored, re...
