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

Abstract

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.