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.
Code-aware prompting: A study of coverage-guided test generation in regression setting using llm
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HyllFuzz uses LLMs to solve constraints and generate modified inputs for hard-to-reach branches in hybrid fuzzing, reporting 31-59% higher branch coverage than CoFuzz, Intriguer and QSYM plus seven new bugs found.
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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.
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Large Language Model assisted Hybrid Fuzzing
HyllFuzz uses LLMs to solve constraints and generate modified inputs for hard-to-reach branches in hybrid fuzzing, reporting 31-59% higher branch coverage than CoFuzz, Intriguer and QSYM plus seven new bugs found.