CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.
Llm test generation via iterative hybrid program analysis
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.SE 3roles
background 1polarities
background 1representative citing papers
By proving test suite coverage is monotone submodular and training LLMs with RL to maximize marginal gains, TestDecision improves branch coverage 38-52% and bug detection up to 95% over base models on ULT and LiveCodeBench.
MUTGEN incorporates mutation feedback into LLM prompts and uses iteration to generate unit tests that achieve higher mutation scores than EvoSuite or vanilla LLM prompting on 204 benchmark subjects.
citing papers explorer
-
Call-Chain-Aware LLM-Based Test Generation for Java Projects
CAT improves line coverage by 18% and branch coverage by 22% over prior LLM test generation methods by adding call-chain and dependency context from static analysis to prompts.
-
TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning
By proving test suite coverage is monotone submodular and training LLMs with RL to maximize marginal gains, TestDecision improves branch coverage 38-52% and bug detection up to 95% over base models on ULT and LiveCodeBench.
-
Mutation-Guided Unit Test Generation with a Large Language Model
MUTGEN incorporates mutation feedback into LLM prompts and uses iteration to generate unit tests that achieve higher mutation scores than EvoSuite or vanilla LLM prompting on 204 benchmark subjects.