---
license: mit
datasets:
- kevinjesse/ManyTypes4TypeScript
metrics:
- accuracy
library_name: transformers
pipeline_tag: text2text-generation
tags:
- code
---

# CodeTIDAL5

We present CodeTIDAL5, a model for type inference on untyped TypeScript / JavaScript!  
The model was introduced as part of the paper

[_Learning Type Inference for Enhanced Dataflow Analysis_](https://arxiv.org/abs/2310.00673)  
Lukas Seidel, Sedick David Baker Effendi, Xavier Pinho, Konrad Rieck, Brink van der Merwe and Fabian Yamaguchi  
ESORICS 2023  

From the abstract:  
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.

## Intended Use
The model was designed for use with the code analysis platform [Joern](https://github.com/joernio/joern).  
As part of the paper, we devise a system which seemlessly integrates type inference recommendations from the CodeTIDAL5 model in Joern's 
Code Property Graphs (CPGs) for enriched context information, aiming at improved taint tracking and dataflow analysis.

An implementation of this approach can be found in the paper's artifact repository:  
https://github.com/joernio/joernti-codetidal5