1 citations · 1 across the 3 of their papers we have counts for
6 papers
How Powerful are LLMs in Generating Formal Program Specifications?
Fanpeng Yang, Xing Li, Shuling Wang +7
Formal verification provides strong guarantees of software correctness, but its adoption is limited by the high cost of writing precise formal specifications. While recent large la…
KBSpec: LLM-driven Formal Specification Generation with Evolving Domain Knowledge Base
Wenhan Wang, Zeyu Sun
Automated formal specification generation is a key step towards program understanding and formal verification. Recently, due to the success of large language models (LLMs) in code…
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…
Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
Wenhan Wang, Xuan Xie, Yuheng Huang +3
Testing autonomous driving systems (ADS) is critical to ensuring their reliability and safety. Existing ADS testing works focuses on designing scenarios to evaluate system-level be…
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…
TESTEVAL: Benchmarking Large Language Models for Test Case Generation
Wenhan Wang, Chenyuan Yang, Zhijie Wang +6
Testing plays a crucial role in the software development cycle, enabling the detection of bugs, vulnerabilities, and other undesirable behaviors. To perform software testing, teste…