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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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.