相关论文: Drop the Golden Apples: Identifying Third-Party Re…
Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from…
The utility of large language models (LLMs) depends heavily on the quality and quantity of their training data. Many organizations possess large data corpora that could be leveraged to train or fine-tune LLMs tailored to their specific…
Software vulnerabilities pose significant security challenges and potential risks to society, necessitating extensive efforts in automated vulnerability detection. There are two popular lines of work to address automated vulnerability…
Compositional verification algorithms are well-studied in the context of model checking. Properly selecting components for verification is important for efficiency, yet has received comparatively less attention. In this paper, we address…
In the landscape of application ecosystems, today's cloud users wish to personalize not only their browsers with various extensions or their smartphones with various applications, but also the various extensions and applications themselves.…
Traceability greatly supports knowledge-intensive tasks, e.g., coverage check and impact analysis. Despite its clear benefits, the \emph{practical} implementation of traceability poses significant challenges, leading to a reduced focus on…
Continual learning (CL) is essential for Large Language Models (LLMs) to adapt to evolving real-world demands, yet they are susceptible to catastrophic forgetting (CF). While traditional CF solutions rely on expensive data rehearsal, recent…
This article introduces SCALPEL3, a scalable open-source framework for studies involving Large Observational Databases (LODs). Its design eases medical observational studies thanks to abstractions allowing concept extraction, high-level…
Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the…
Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring intelligent automation to detect, assess, and remediate vulnerabilities more efficiently and…
Android app developers extensively employ code reuse, integrating many third-party libraries into their apps. While such integration is practical for developers, it can be challenging for static analyzers to achieve scalability and…
Application-level caches are widely adopted by web applications to minimize the response time of user requests as well as to reduce the burden on the system backend, such as the database servers. In the state of practice, developers have to…
Alleviating noisy pseudo labels remains a key challenge in Semi-Supervised Temporal Action Localization (SS-TAL). Existing methods often filter pseudo labels based on strict conditions, but they typically assess classification and…
Software bloat refers to code and features that is not used by a software during runtime. For Machine Learning (ML) systems, bloat is a major contributor to their technical debt leading to decreased performance and resource wastage. In this…
Given a table T in a database and a question Q in natural language, the table question answering (TQA) task aims to return an accurate answer to Q based on the content of T. Recent state-of-the-art solutions leverage large language models…
Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap,…
Large Language Models are a promising tool for automated vulnerability detection, thanks to their success in code generation and repair. However, despite widespread adoption, a critical question remains: Are LLMs truly effective at…
In this paper, we introduce a general extension of linear sparse component analysis (SCA) approaches to postnonlinear (PNL) mixtures. In particular, and contrary to the state-of-art methods, our approaches use a weak sparsity source…
Ensuring the security of modern System-on-Chip (SoC) designs poses significant challenges due to increasing complexity and distributed assets across the intellectual property (IP) blocks. Formal property verification (FPV) provides the…
One of the most interesting tools that have recently entered the data science toolbox is topological data analysis (TDA). With the explosion of available data sizes and dimensions, identifying and extracting the underlying structure of a…