Ask Me Any Type:Type Inference Plugin for Partial Code on the Web and in the Integrated Development Environment

Inferring the fully qualified names(FQNs)of undeclared receiving objects and non-fully-qualified type names(non-FQNs)in partial code is critical for effectively searching,understanding,and reusing partial code.Existing type inference tools,such as COSTER and SNR,rely on a symbolic knowledge base and adopt a dictionary-lookup strategy to map simple names of undeclared receiving objects and non-FQNs to FQNs.However,building a symbolic knowledge base requires parsing compilable code files,which limits the collection of APIs and code contexts,resulting in out-of-vocabulary(OOV)failures.To overcome the limitations of a symbolic knowledge base for FQN inference,we implemented Ask Me Any Type(AMAT),a type of inference plugin embedded in web browsers and integrated development environment(IDE).Unlike the dictionary-lookup strategy,AMAT uses a cloze-style fill-in-the-blank strategy for type inference.By treating code as text,AMAT leverages a fine-tuned large language model(LLM)as a neural knowledge base,thereby preventing the need for code compilation.Experimental results show that AMAT outperforms state-of-the-art tools such as COSTER and SNR.In practice,developers can directly reuse partial code by inferring the FQNs of unresolved type names in real time.

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