paper-with-me

Papers

C2RUST-BENCH: A Minimized, Representative Dataset for C-to-Rust Transpilation Evaluation

2025-04-21 · Melih Sirlanci, Carter Yagemann, Zhiqiang Lin

Despite the effort in vulnerability detection over the last two decades, memory safety vulnerabilities continue to be a critical problem. Recent reports suggest that the key solution is to migrate to memory-safe languages. To this end, C-to-Rust transpilation becomes popular to resolve memory-safety issues in C programs. Recent works propose C-to-Rust transpilation frameworks; however, a comprehensive evaluation dataset is missing. Although one solution is to put together a large enough dataset, this increases the analysis time in automated frameworks as well as in manual efforts for some cases. In this work, we build a method to select functions from a large set to construct a minimized yet representative dataset to evaluate the C-to-Rust transpilation. We propose C2RUST-BENCH that contains 2,905 functions, which are representative of C-to-Rust transpilation, selected from 15,503 functions of real-world programs.

📄 PDF Abstract BibTeX arXiv:2504.15144

Code (0)

등록된 구현이 없습니다.

Tasks

Vulnerability Detection

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

Similar Papers 제목 키워드 기반

RoboTrustBench: Benchmarking the Trustworthiness of Video World Models for Robotic Manipulation

2026-06-01 · Huiqiong Li, Jiayu Wang, Zhiting Mei, Anirudha Majumdar 외 arxiv

Video world models are increasingly used in robotic manipulation, yet existing benchmarks mostly evaluate them under valid, feasible, and safe instructions. We introduce RoboTrustBench, a benchmark for evaluating the tru…

Instruction Following

In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions

2025-10-17 · Aria Pessianzadeh, Naima Sultana, Hildegarde Van den Bulck, David Gefen 외 arxiv

The rise of generative AI (GenAI) has impacted many aspects of human life. As these systems become embedded in everyday practices, understanding public trust in them is also essential for responsible adoption and governa…

Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI

2026-03-16 · Jinhu Qi, Yifan Li, Minghao Zhao, Wentao Zhang 외 arxiv

Agentic AI systems increasingly act through tool-augmented, multi-step workflows whose failures (unsafe tool use, unauthorised actions, social harm) carry deployment-level consequences. Evaluation practice remains fragme…

CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation

2025-04-21 · Anirudh Khatry, Robert Zhang, Jia Pan, Ziteng Wang 외

C-to-Rust transpilation is essential for modernizing legacy C code while enhancing safety and interoperability with modern Rust ecosystems. However, no dataset currently exists for evaluating whether a system can transpi…

Code Translation

TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction

2026-06-15 · Bohao Liao, Boyu Deng, Qipeng Song, Jieling Wang 외 arxiv

Trust prediction infers latent user-user trust relations and provides important support for social recommendation, fake-review and manipulation detection, and risk identification. Graph neural networks have become a prom…

Graph Learning