PLLM+ pipeline solves 1,500 Python dependency issues in benchmark
Researchers introduced PLLM+, a hybrid pipeline for resolving Python dependency conflicts. The system combines deterministic steps—like static AST analysis, replaying past solutions from a database, and live PyPI validation—before using LLM-based repair for unresolved cases.
Key points
- PLLM+ solves 1,500 of 2,891 dependency-failing Python snippets in HG2.9K benchmark
- Reduces average runtime from 368.7 to 71.8 seconds per snippet
- 1,495 fixes come from replaying validated configurations, not LLM repair
The approach prioritizes efficiency, cutting average runtime from 368.7 seconds to 71.8 seconds per snippet. Most fixes (1,495) rely on replaying validated configurations from a solutions database, while LLM-based repair handles only 5 additional fixes. The paper suggests deterministic reuse of prior solutions is often more effective than LLM-based repair alone.
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