activity
20232026
most citedBenchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities

31 citations · 48 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SE2026

C2RustXW: Program-Structure-Aware C-to-Rust Translation via Program Analysis and LLM

Yanyan Yan, Yang Feng, Jiangshan Liu +4

The growing adoption of Rust for its memory safety and performance has increased the demand for effective migration of legacy C codebases. However, existing rule-based translators…

cs.SE2025

Large Language Models for Unit Test Generation: Achievements, Challenges, and Opportunities

Bei Chu, Yang Feng, Kui Liu +5

Unit tests help detect faults and prevent regressions, but writing them manually is costly. Automated unit test generation involves constructing inputs that exercise the code and o…

cs.SE2025★ 1 cited

PALM: Synergizing Program Analysis and LLMs to Enhance Rust Unit Test Coverage

Bei Chu, Yang Feng, Kui Liu +4

Unit testing is essential for ensuring software reliability and correctness. Classic Search-Based Software Testing (SBST) methods and concolic execution-based approaches for genera…

cs.PL2025

An Empirical Study of Rust-Specific Bugs in the rustc Compiler

Zixi Liu, Yang Feng, Yunbo Ni +5

Rust is gaining popularity for its well-known memory safety guarantees and high performance, distinguishing it from C/C++ and JVM-based languages. Its compiler, rustc, enforces the…

cs.SE2024

PanicFI: An Infrastructure for Fixing Panic Bugs in Real-World Rust Programs

Yunbo Ni, Yang Feng, Zixi Liu +2

The Rust programming language has garnered significant attention due to its robust safety features and memory management capabilities. Despite its guaranteed memory safety, Rust pr…

cs.SE2024★ 16 cited

MultiTest: Physical-Aware Object Insertion for Testing Multi-sensor Fusion Perception Systems

Xinyu Gao, Zhijie Wang, Yang Feng +3

Multi-sensor fusion stands as a pivotal technique in addressing numerous safety-critical tasks and applications, e.g., self-driving cars and automated robotic arms. With the contin…

cs.SE2023★ 31 cited

Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities

Xinyu Gao, Zhijie Wang, Yang Feng +3

Multi-Sensor Fusion (MSF) based perception systems have been the foundation in supporting many industrial applications and domains, such as self-driving cars, robotic arms, and unm…