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

Vulnerability detection is a critical problem in software security and
attracts growing attention both from academia and industry. Traditionally,
software security is safeguarded by designated rule-based detectors that
heavily rely on empirical expertise, requiring tremendous effort from software
experts to generate rule repositories for large code corpus. Recent advances in
deep learning, especially Graph Neural Networks (GNN), have uncovered the
feasibility of automatic detection of a wide range of software vulnerabilities.
However, prior learning-based works only break programs down into a sequence of
word tokens for extracting contextual features of codes, or apply GNN largely
on homogeneous graph representation (e.g., AST) without discerning complex
types of underlying program entities (e.g., methods, variables). In this work,
we are one of the first to explore heterogeneous graph representation in the
form of Code Property Graph and adapt a well-known heterogeneous graph network
with a dual-supervisor structure for the corresponding graph learning task.
Using the prototype built, we have conducted extensive experiments on both
synthetic datasets and real-world projects. Compared with the state-of-the-art
baselines, the results demonstrate promising effectiveness in this research
direction in terms of vulnerability detection performance (average F1
improvements over 10\% in real-world projects) and transferability from C/C++
to other programming languages (average F1 improvements over 11%).

本文介绍了使用异构图表征和双监督结构的 Well-known 网络进行对应的图学习任务，从而实现软件漏洞的检测，通过使用原型进行实验，表明该方法对于检测性能具有良好的有效性，并在从 C/C++ 到其他编程语言的转换性上优于现有技术。

DSHGT: 双监督异构图变换器 —— 使用异构图学习检测软件漏洞的先驱研究

DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer  study of using heterogeneous graph learning for detecting software  vulnerabilities