4 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.SE2024★ 1 cited
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…
cs.SE2023★ 2 cited
ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
Zejun Wang, Jia Li, Ge Li +1
Large language models have shown good performances in generating code to meet human requirements. However, human requirements expressed in natural languages can be vague, incomplet…
cs.SE2023★ 4 cited
SAGA: Summarization-Guided Assert Statement Generation
Yuwei Zhang, Zhi Jin, Zejun Wang +2
Generating meaningful assert statements is one of the key challenges in automated test case generation, which requires understanding the intended functionality of the tested code.…