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--- |
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license: mit |
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datasets: |
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- kevinjesse/ManyTypes4TypeScript |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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tags: |
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- code |
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--- |
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# CodeTIDAL5 |
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We present CodeTIDAL5, a model for type inference on untyped TypeScript / JavaScript! |
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The model was introduced as part of the paper |
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[_Learning Type Inference for Enhanced Dataflow Analysis_](https://arxiv.org/abs/2310.00673) |
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Lukas Seidel, Sedick David Baker Effendi, Xavier Pinho, Konrad Rieck, Brink van der Merwe and Fabian Yamaguchi |
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ESORICS 2023 |
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From the abstract: |
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We propose CodeTIDAL5, a Transformer-based model trained to reliably |
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predict type annotations. For effective result retrieval and re-integration, |
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we extract usage slices from a program’s code property graph. |
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Comparing our approach against recent neural type inference systems, our |
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model outperforms the current state-of-the-art by 7.85% on the ManyTypes4TypeScript benchmark, achieving 71.27% accuracy overall. |
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## Intended Use |
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The model was designed for use with the code analysis platform [Joern](https://github.com/joernio/joern). |
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As part of the paper, we devise a system which seemlessly integrates type inference recommendations from the CodeTIDAL5 model in Joern's |
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Code Property Graphs (CPGs) for enriched context information, aiming at improved taint tracking and dataflow analysis. |
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An implementation of this approach can be found in the paper's artifact repository: |
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https://github.com/joernio/joernti-codetidal5 |
