一句话：利用注释、函数名与参数名等自然语言信息，以 LSTM 预测 JavaScript 函数类型签名，并可用于检查类型注解一致性。
JavaScript 动态类型语言缺少静态类型安全，导致 IDE 支持不佳、API 难理解与意外运行时行为；Flow、TypeScript 等渐进类型系统依赖开发者手动标注。本文提出 NL2Type，基于学习预测 JavaScript 函数类型签名，核心思想是利用注释、函数名、参数名等常被类型推断算法忽略的自然语言信息。方法将类型预测形式化为分类问题，训练 LSTM 循环神经模型，在带标注代码库上学习后为未标注代码预测函数类型。在 162,673 个真实项目 JavaScript 文件上，top-1 建议精确率 84.1%、召回率 78.9%，top-5 精确率 95.5%、召回率 89.6%，优于 JSNice 与 DeepTyper。NL2Type 还可充当已有类型注解的一致性检查器，在人工分析 50 条警告后发现 39 个值得关注的不一致，多因类型注解错误。
JavaScript is dynamically typed and hence lacks the type safety of statically typed languages, leading to suboptimal IDE support, difficult to understand APIs, and unexpected runtime behavior. Several gradual type systems have been proposed, e.g., Flow and TypeScript, but they rely on developers to annotate code with types. This paper presents NL2Type, a learning-based approach for predicting likely type signatures of JavaScript functions. The key idea is to exploit natural language information in source code, such as comments, function names, and parameter names, a rich source of knowledge that is typically ignored by type inference algorithms. We formulate the problem of predicting types as a classification problem and train a recurrent, LSTM-based neural model that, after learning from an annotated code base, predicts function types for unannotated code. We evaluate the approach with a corpus of 162,673 JavaScript files from real-world projects. NL2Type predicts types with a precision of 84.1% and a recall of 78.9% when considering only the top-most suggestion, and with a precision of 95.5% and a recall of 89.6% when considering the top-5 suggestions. The approach outperforms both JSNice, a state-of-the-art approach that analyzes implementations of functions instead of natural language information, and DeepTyper, a recent type prediction approach that is also based on deep learning. Beyond predicting types, NL2Type serves as a consistency checker for existing type annotations. We show that it discovers 39 inconsistencies that deserve developer attention (from a manual analysis of 50 warnings), most of which are due to incorrect type annotations.