问我任何类型:嵌入Web浏览器和集成开发环境(IDE)的代码片段类型推理插件

扫码查看

原文链接

NETL
NSTL
万方数据

中文摘要：推理代码片段中未声明的接收对象和非完全限定类型名称(非FQNs)的完全限定名称(FQNs)对于有效搜索、理解和重用代码片段至关重要.现有的类型推断工具,如COSTER和SNR,依赖于符号知识库并采用字典查找策略,将未声明的接收对象和非FQNs的简单名称映射到FQNs.然而,构建符号知识库需要解析可编译的代码文件,它限制了API和代码上下文的收集,导致待搜索的FQN不在符号知识库范围.为克服符号知识库在FQN推理中的局限性,本文实现了一种嵌入Web浏览器和集成开发环境(IDE)的类型推理插件 Ask-Me-Any-Type(AMAT).AMAT使用填空式策略而不是字典查找策略进行类型推理,通过将代码视为文本,把经过微调的大型语言模型(LLM)作为神经知识库,避免了代码编译的需要.实验结果表明,AMAT的性能优于COSTER和SNR等工具.在实践中,开发人员可以运用AMAT实时推理未解析类型名称的FQNs,直接重用代码片段.

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

外文摘要：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 develop-ment 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,de-velopers can directly reuse partial code by inferring the FQNs of unresolved type names in real time.

外文关键词：

type inferencelarge language modelprompt learningweb and integrated development environment(IDE)plugin

作者：

程煜、黄冠鸣、吴贻顺、赵梓杰、何祯豪、卢家兴

展开 >

作者单位：

江西师范大学计算机信息工程学院,江西南昌 330022

江西师范大学附属中学,江西南昌 330013

关键词：

类型推理大型语言模型提示学习网页和集成开发环境插件

基金：

Key Scientific and Technological Research Projects of the Jiangxi Provincial Department of EducationNational Social Science Foundation Major Bidding Project

项目编号：

GJJ220030320&ZD068

出版年：

2024

DOI：

10.1051/wujns/2024294349

武汉大学自然科学学报(英文版)

武汉大学

武汉大学自然科学学报(英文版)

CSTPCD

影响因子：0.066

ISSN：1007-1202

年,卷(期)：2024.29(4)