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Partial code usually involves non-fully-qualified type names (non-FQNs) andundeclared receiving objects. Resolving the FQNs of these non-FQN types andundeclared receiving objects (referred to as type inference) is theprerequisite to effective search and reuse of partial code. Existingdictionary-lookup based methods build a symbolic knowledge base of API namesand code contexts, which involve significant compilation overhead and aresensitive to unseen API names and code context variations. In this paper, weformulate type inference as a cloze-style fill-in-blank language task. Built onsource code naturalness, our approach fine-tunes a code masked language model(MLM) as a neural knowledge base of code elements with a novel "pre-train,prompt and predict" paradigm from raw source code. Our approach is lightweightand has minimum requirements on code compilation. Unlike existing symbolic nameand context matching for type inference, our prompt-tuned code MLM packs FQNsyntax and usage in its parameters and supports fuzzy neural type inference. Wesystematically evaluate our approach on a large amount of source code fromGitHub and Stack Overflow. Our results confirm the effectiveness of ourapproach design and the practicality for partial code type inference. As thefirst of its kind, our neural type inference method opens the door to manyinnovative ways of using partial code.

Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code