Statically analyzing dynamically-typed code is a challenging endeavor, as
even seemingly trivial tasks such as determining the targets of procedure calls
are non-trivial without knowing the types of objects at compile time.
Addressing this challenge, gradual typing is increasingly added to
dynamically-typed languages, a prominent example being TypeScript that
introduces static typing to JavaScript. Gradual typing improves the developer's
ability to verify program behavior, contributing to robust, secure and
debuggable programs. In practice, however, users only sparsely annotate types
directly. At the same time, conventional type inference faces
performance-related challenges as program size grows. Statistical techniques
based on machine learning offer faster inference, but although recent
approaches demonstrate overall improved accuracy, they still perform
significantly worse on user-defined types than on the most common built-in
types. Limiting their real-world usefulness even more, they rarely integrate
with user-facing applications. We propose CodeTIDAL5, a Transformer-based model
trained to reliably predict type annotations. For effective result retrieval
and re-integration, we extract usage slices from a program's code property
graph. Comparing our approach against recent neural type inference systems, our
model outperforms the current state-of-the-art by 7.85% on the
ManyTypes4TypeScript benchmark, achieving 71.27% accuracy overall. Furthermore,
we present JoernTI, an integration of our approach into Joern, an open source
static analysis tool, and demonstrate that the analysis benefits from the
additional type information. As our model allows for fast inference times even
on commodity CPUs, making our system available through Joern leads to high
accessibility and facilitates security research.

CodeTIDAL5 是一种基于 Transformer 的模型，用于可靠地预测类型注释，并从程序的代码属性图中提取使用片段，其在 ManyTypes4TypeScript 基准测试上优于当前最先进的神经类型推理系统 7.85％，总体准确率达到 71.27％。

增强数据流分析的学习类型推断

Learning Type Inference for Enhanced Dataflow Analysis

As gradual typing becomes increasingly popular in languages like Python and
TypeScript, there is a growing need to infer type annotations automatically.
While type annotations help with tasks like code completion and static error
catching, these annotations cannot be fully determined by compilers and are
tedious to annotate by hand. This paper proposes a probabilistic type inference
scheme for TypeScript based on a graph neural network. Our approach first uses
lightweight source code analysis to generate a program abstraction called a
type dependency graph, which links type variables with logical constraints as
well as name and usage information. Given this program abstraction, we then use
a graph neural network to propagate information between related type variables
and eventually make type predictions. Our neural architecture can predict both
standard types, like number or string, as well as user-defined types that have
not been encountered during training. Our experimental results show that our
approach outperforms prior work in this space by $14\%$ (absolute) on library
types, while having the ability to make type predictions that are out of scope
for existing techniques.

本文提出了一种基于概率类型推断和图神经网络的 TypeScript 类型推断方案，采用源代码分析生成类型依赖图，并在相关的类型变量之间传递信息来进行类型预测，可预测标准类型和用户自定义类型，并且比现有技术在库类型上表现提高了 14％。