Ask Me Any Type: Type Inference Plugin for Partial Code on the Web and in the Integrated Development Environment.
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| Title: | Ask Me Any Type: Type Inference Plugin for Partial Code on the Web and in the Integrated Development Environment. |
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| Authors: | CHENG Yu, HUANG Guanming, WU Yishun, ZHAO Zijie, HE Zhenhao, LU Jiaxing |
| Source: | Wuhan University Journal of Natural Sciences; Aug2024, Vol. 29 Issue 4, p349-356, 8p |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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