Xiaoxue Wu

RTL-level fuzz testing is critical for identifying vulnerabilities in hardware designs. However, existing hardware fuzz testing methods suffer from slow coverage improvement and blind exploration due to the lack of fine-grained control flow guidance. To address this gap, this article proposes the CFGuide-Fuzz framework, which includes control node extraction and compression techniques based on FIRRTL instrument and a hardware fuzz engine driven by feature feedback. This research introduces a fine-grained control flow feedback mechanism for hardware fuzz testing, enabling a pivotal shift from blind exploration to targeted testing. Experimental results demonstrate that compared to the DiFuzzRTL baseline, the proposed CFGuide-Fuzz framework enhances register state coverage by 9.4% under identical iteration counts and testing environments. Additionally, it doubles the number of effective inputs that trigger mismatched differential test results. These findings fully validate the framework’s dual advantages: deeper hardware control flow exploration and higher semantic vulnerability triggering efficiency.

The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across multiple sectors, including Software Engineering (SE). However, due to their black-box nature, these promising AI-driven SE models are still far from being deployed in practice. This lack of explainability poses unwanted risks for their applications in critical tasks, such as vulnerability detection, where decision-making transparency is of paramount importance. This article endeavors to elucidate this interdisciplinary domain by presenting a systematic literature review of approaches that aim to improve the explainability of AI models within the context of SE. The review canvasses work appearing in the most prominent SE and AI conferences and journals, and spans 108 articles across 23 unique SE tasks. Based on three key Research Questions (RQs), we aim to (1) summarize the SE tasks where XAI techniques have shown success to date; (2) classify and analyze different XAI techniques; and (3) investigate existing evaluation approaches. Based on our findings, we identified a set of challenges remaining to be addressed in existing studies, together with a set of guidelines highlighting potential opportunities we deemed appropriate and important for future work.

An operating system is the essence of software, serving as the foundation for the operation of various application software. The security of the operating system is crucial for national informatization construction. Data indicate that many cybersecurity incidents result from exploiting security vulnerabilities in the operating system. Linux is currently the most widely used open‐source operating system, with thousands of Common Vulnerabilities and Exposures (CVEs) related to Linux systems reported each year. Therefore, research and prevention of vulnerabilities in the Linux system are particularly important. To gain a better understanding of the characteristics of Linux system vulnerabilities, this paper leverages knowledge in the field of software security to analyze nearly 10,000 historical vulnerability data in two core systems of Linux: Linux Kernel and Debian Linux. The study explores the evolutionary patterns of vulnerability characteristics. Specific research contents include the following: (1) data collection and cleaning of vulnerability data in Linux Kernel and Debian Linux systems; (2) cross‐statistical analysis of structured data features in vulnerability reports; (3) unstructured data characteristics mining in vulnerability reports based on domain knowledge; (4) analysis of the evolution of vulnerability characteristics. This paper provides empirical lessons and guidance for Linux system vulnerabilities to assist practitioners and researchers in better preventing and detecting vulnerabilities in Linux and Linux‐based systems.

Vulnerability detection plays a crucial role in the software development lifecycle. Commit-level vulnerability detection aims to detect whether the changed code contributed to potential vulnerabilities by the developer when submitting the code, which is also referred to as Just-In-Time (JIT) vulnerability detection. Previous JIT vulnerability detection approaches relied on code metrics and textual features, which were unable to effectively characterize vulnerability-contributing commits (VCCs). Recently, CodeJIT (a code-centric learning-based approach) has been proposed to detect vulnerability at the commit-level. However, CodeJIT still has its limitations: imprecise feature representation, static code embedding, and underutilized heterogeneous information. In this paper, we propose HgtJIT, a JIT vulnerability detection approach based on a Heterogeneous Graph Transformer (HGT) in order to address several limitations of the state-of-the-art CodeJIT approach. We propose diffPDG to represent code changes and use the CCT5 model (the latest feature encoder pre-trained on a large-scale code change corpus) to embed graph nodes to generate the most meaningful vector representations. In addition, we employ HGT to adequately utilize heterogeneous information of the graph to learn vulnerability features. Extensive experiments have shown that HgtJIT is the best-performing model, with F1 and AUC improvement of 14.6%-37.5% and 12.2%-53.7% compared to the baseline model