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API documentation, technical blogs and programming Q&A sites contain numerouspartial code that can be reused in programming tasks, but often these code areuncompilable due to unresolved names and syntax errors. To facilitate partialcode reuse, we propose the Partial Code Reuse Chain (PCR-Chain) for resolvingfully-qualified names (FQNs) and fixing last-mile syntax errors in partial codebased on a giant large language model (LLM) like ChatGPT. Methodologically,PCR-Chain is backed up by the underlying global-level prompt architecture(which combines three design ideas: hierarchical task breakdown, promptcomposition, and a mix of prompt-based AI and non-AI units) and the local-levelprompt design. Technically, we propose PCR-Chain, which employs in-contextlearning rather than symbolic, costly training methods. Experimental resultsdemonstrate that in dynamically-typed languages (Python), PCR-Chain outperformscurrent state-of-the-art (SOTA) 5% accuracy like RING. For statically-typelanguages (Java), our approach achieves high accuracy of 80.5% in resolvingboth non-FQNs and last-mile syntax errors, surpassing SOTA methods (RING) thatcan only address last-mile syntax errors. The correct execution of the unit,module, and PCR-Chain demonstrates the effectiveness of the prompt design,composition, and architecture and opens up possibilities for building softwareengineering tools based on LLMs, replacing traditional program analysismethods.

A Chain of AI-based Solutions for Resolving FQNs and Fixing Syntax Errors in Partial Code