Merlin generates CodeQL queries from natural language questions via RAG-based iteration and a self-test technique using assistive queries, achieving 3.8x higher task accuracy and 31% less completion time in user studies while finding additional software issues.
preprint arXiv:2408.03095(2024)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.SE 3roles
background 2polarities
background 2representative 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
-
Generating Complex Code Analyzers from Natural Language Questions
Merlin generates CodeQL queries from natural language questions via RAG-based iteration and a self-test technique using assistive queries, achieving 3.8x higher task accuracy and 31% less completion time in user studies while finding additional software issues.
-
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.