Related papers: Data Preparation for Software Vulnerability Predic…
Software vulnerabilities (SVs) have emerged as a prevalent and critical concern for safety-critical security systems. This has spurred significant advancements in utilizing AI-based methods, including machine learning and deep learning, for…
Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software…
The growing reliance on software in road vehicles has led to the emergence of Software-Defined Vehicles (SDV). This work analyzes SDV security and privacy through a systematic literature review complemented by an industry questionnaire…
Software vulnerabilities can result in catastrophic cyberattacks that increasingly threaten business operations. Consequently, ensuring the safety of software systems has become a paramount concern for both private and public sectors.…
Preventing vulnerability exploits is a critical software maintenance task, and software engineers often rely on Common Vulnerability and Exposure (CVEs) reports for information about vulnerable systems and libraries. These reports include…
Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades. Traditional code analysis methods have been proposed, but are often ineffective and inefficient. In this work, we model…
[Background] The MVP concept has influenced the way in which development teams apply Software Engineering practices. However, the overall understanding of this influence of MVPs on SE practices is still poor. [Objective] Our goal is to…
Collecting relevant and high-quality data is integral to the development of effective Software Vulnerability (SV) prediction models. Most of the current SV datasets rely on SV-fixing commits to extract vulnerable functions and lines.…
The use of learning-based techniques to achieve automated software vulnerability detection has been of longstanding interest within the software security domain. These data-driven solutions are enabled by large software vulnerability…
Software vulnerabilities remain a significant risk factor in achieving security objectives within software development organizations. This is especially true where either proprietary or open-source software (OSS) is included in the…
The increasing prevalence of software vulnerabilities necessitates automated vulnerability repair (AVR) techniques. This Systematization of Knowledge (SoK) provides a comprehensive overview of the AVR landscape, encompassing both synthetic…
The integration of open-source third-party library dependencies in Java development introduces significant security risks when these libraries contain known vulnerabilities. Existing Software Composition Analysis (SCA) tools struggle to…
Context: Software testing plays an essential role in product quality improvement. For this reason, several software testing models have been developed to support organizations. However, adoption of testing process models inside…
This paper presents a framework that selectively triggers security reviews for incoming source code changes. Functioning as a review bot within a code review service, the framework can automatically request additional security reviews at…
Software vulnerabilities can have serious consequences, which is why many techniques have been proposed to defend against them. Among these, vulnerability detection techniques are a major area of focus. However, there is a lack of a…
In this paper, we study randomized methods for feedback design of uncertain systems. The first contribution is to derive the sample complexity of various constrained control problems. In particular, we show the key role played by the…
This paper presents a preliminary validation of common textual information retrieval techniques for mapping unstructured software vulnerability information to distinct software weaknesses. The validation is carried out with a dataset…
In cybersecurity, vulnerability assessment has typically focused on identifying and measuring vulnerabilities within digital assets and technical infrastructures. However, there is growing recognition that this approach alone is inadequate…
Software ecosystems built around programming languages have greatly facilitated software development. At the same time, their security has increasingly been acknowledged as a problem. To this end, the paper examines the previously…
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of…