# Inferring Pluggable Types with Machine Learning

arXiv: https://arxiv.org/abs/2406.15676 · HTML: https://mlsanity.com/paper/2406.15676/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 56 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 23 of 23)

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- [SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents](https://mlsanity.com/paper/2406.12952/) (2024)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [AI-assisted coding: Experiments with GPT-4](https://mlsanity.com/paper/2304.13187/) (2023)
- [TypeWriter: Neural Type Prediction with Search-based Validation](https://mlsanity.com/paper/1912.03768/) (2019)
- [Exploring the Boundaries of GPT-4 in Radiology](https://mlsanity.com/paper/2310.14573/) (2023)
- [Typilus: Neural Type Hints](https://mlsanity.com/paper/2004.10657/) (2020)
- [Large Language Models in Fault Localisation](https://mlsanity.com/paper/2308.15276/) (2023)
- [Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs](https://mlsanity.com/paper/2106.06218/) (2021)
- [Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python](https://mlsanity.com/paper/2101.04470/) (2021)
- [Static Inference Meets Deep Learning: A Hybrid Type Inference Approach for Python](https://mlsanity.com/paper/2105.03595/) (2021)
- [The Program Testing Ability of Large Language Models for Code](https://mlsanity.com/paper/2310.05727/) (2023)
- [Comparing large language models and human programmers for generating programming code](https://mlsanity.com/paper/2403.00894/) (2024)
- [Exploring the Efficacy of Large Language Models (GPT-4) in Binary Reverse Engineering](https://mlsanity.com/paper/2406.06637/) (2024)
- [PYInfer: Deep Learning Semantic Type Inference for Python Variables](https://mlsanity.com/paper/2106.14316/) (2021)
- [NullAway: Practical Type-Based Null Safety for Java](https://mlsanity.com/paper/1907.02127/) (2019)
- [Solver-based Gradual Type Migration](https://mlsanity.com/paper/2109.05049/) (2021)
- [Semantics for Locking Specifications](https://mlsanity.com/paper/1501.05338/) (2015)

## Abstract (arXiv)

Pluggable type systems allow programmers to extend the type system of a programming language to enforce semantic properties defined by the programmer. Pluggable type systems are difficult to deploy in legacy codebases because they require programmers to write type annotations manually. This paper investigates how to use machine learning to infer type qualifiers automatically. We propose a novel representation, NaP-AST, that encodes minimal dataflow hints for the effective inference of type qualifiers. We evaluate several model architectures for inferring type qualifiers, including Graph Transformer Network, Graph Convolutional Network and Large Language Model. We further validated these models by applying them to 12 open-source programs from a prior evaluation of the NullAway pluggable typechecker, lowering warnings in all but one unannotated project. We discovered that GTN shows the best performance, with a recall of .89 and precision of 0.6. Furthermore, we conduct a study to estimate the number of Java classes needed for good performance of the trained model. For our feasibility study, performance improved around 16k classes, and deteriorated due to overfitting around 22k classes.