SMT-LLM builds a constraint graph from PyPI metadata and AST-derived imports, solves it with Z3, and uses LLM imputation only when needed, resolving 83.6% of HG2.9K snippets versus PLLM's 54.8% while cutting median time by 6.3x and LLM calls by 11x.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Breaking the Dependency Chaos: A Constraint-Driven Python Dependency Resolution Strategy with Selective LLM Imputation
SMT-LLM builds a constraint graph from PyPI metadata and AST-derived imports, solves it with Z3, and uses LLM imputation only when needed, resolving 83.6% of HG2.9K snippets versus PLLM's 54.8% while cutting median time by 6.3x and LLM calls by 11x.