3 citations · 5 across the 6 of their papers we have counts for
6 papers
Dependency-Guided Repository-Level C-to-Rust Translation with Reinforcement Alignment
Jia Feng, Wenjie Gan, Cuiyun Gao +5
Automating C-to-Rust migration is critical for improving software security without sacrificing performance. Traditional rule-based methods struggle with diverse C idioms, often pro…
Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy
Yihong Dong, Jianha Xiao, Xue Jiang +7
The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack systematic evaluation based on comput…
Can Large Language Models Reason About Complex Execution Paths? An Empirical Study on Python
Wenhan Wang, Kaibo Liu, Zeyu Sun +4
Execution path reasoning is a key step towards program semantics understanding. It is crucial for generating test cases that cover certain branches/paths, or detecting bugs that ar…
Python Symbolic Execution with LLM-powered Code Generation
Wenhan Wang, Kaibo Liu, An Ran Chen +4
Symbolic execution is a key technology in software testing, which generates test cases by collecting symbolic path constraints and then solving constraints with SMT solvers. Symbol…
HITS: High-coverage LLM-based Unit Test Generation via Method Slicing
Zejun Wang, Kaibo Liu, Ge Li +1
Large language models (LLMs) have behaved well in generating unit tests for Java projects. However, the performance for covering the complex focal methods within the projects is po…
LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
Kaibo Liu, Zhenpeng Chen, Yiyang Liu +7
Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this probl…