As Large Language Models (LLMs) evolve in understanding and generating code, accurately evaluating their reliability in analyzing source code vulnerabilities becomes in-creasingly vital. While studies have examined LLM capabilities in tasks like vulnerability detection and repair, they often over-look the importance of both structure and semantic reasoning crucial for trustworthy vulnerability analysis. To address this gap, we introduce SV-TRUSTEVAL-C, a benchmark designed to evaluate LLMs' abilities for vulnerability analysis of code written in the C programming language through two key di-mensions: structure reasoning-assessing how models identify relationships between code elements under varying data and control flow complexities; and semantic reasoning-examining their logical consistency in scenarios where code is structurally and semantically perturbed. Our results show that current LLMs are far from satisfactory in understanding complex code relationships and that their vulnerability analyses rely more on pattern matching than on robust logical reasoning. These findings underscore the effectiveness of the SV-TRUSTEVAL-C benchmark and highlight critical areas for enhancing the reasoning capabilities and trustworthiness of LLMs in real-world vulnerability analysis tasks. Our ini-tial benchmark dataset is available at https://huggingface.co/datasets/LLMs4CodeSecurity/SV-TrustEval-C-1.0

We introduce in this paper a scalable implementation of Variational Bayesian Matrix Factorization method for collaborative filtering using the MapReduce framework. Variational Bayesian methods have the advantage of providing good approximate analytical solutions for the posterior distribution. Due to the independence assumption about the parameters in the posterior distribution, variational methods are also likely to be able to parallelize efficiently. Though Variational Bayesian Matrix Factorization method has shown to produce more accurate results in collaborative filtering, its scaling properties have not studied so far. We ran our MapReduce implementation on the CiteULike data set and show that our parallelization scheme achieves approximately linear scaling. We also compare its performance with the MapReduce implementation of a popular matrix factorization algorithm, ALSWR, from the open source machine learning library Mahout.