3 citations · 4 across the 4 of their papers we have counts for
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cs.SE2025
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…
cs.SE2024★ 1 cited
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…
cs.SE2024★ 3 cited
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…