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.

基于插件的集成建模环境原型开发研究

集成建模环境主要用于模型集成的快速实现,提高建模的真实性和模型预测分析能力,其归根到底是模型的拆与装的问题,即如何将现有模型模块化,然后将合适的模块在建模环境的支持下重新组装到一起。讨论了基于插件的集成建模环境原型开发方法,建立了一个可视化的集成建模环境,通过图形化操作,该环境能实现简单的水文水资源模型合成和连接,生成新的模型。