# OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints

arXiv: https://arxiv.org/abs/2004.00348 · HTML: https://mlsanity.com/paper/2004.00348/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python](https://mlsanity.com/paper/2101.04470/) (2021)
- [TypeT5: Seq2seq Type Inference using Static Analysis](https://mlsanity.com/paper/2303.09564/) (2023)
- [CodeQueries: A Dataset of Semantic Queries over Code](https://mlsanity.com/paper/2209.08372/) (2022)
- [ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback](https://mlsanity.com/paper/2107.14035/) (2021)
- [Type Prediction With Program Decomposition and Fill-in-the-Type Training](https://mlsanity.com/paper/2305.17145/) (2023)
- [Understanding How CodeLLMs (Mis)Predict Types with Activation Steering](https://mlsanity.com/paper/2404.01903/) (2024)
- [AgenticTyper: Automated Typing of Legacy Software Projects Using Agentic AI](https://mlsanity.com/paper/2602.21251/) (2026)
- [Cross-Domain Evaluation of a Deep Learning-Based Type Inference System](https://mlsanity.com/paper/2208.09189/) (2022)
- [The Power of Types: Exploring the Impact of Type Checking on Neural Bug Detection in Dynamically Typed Languages](https://mlsanity.com/paper/2411.15368/) (2024)
- [Learning Type Inference for Enhanced Dataflow Analysis](https://mlsanity.com/paper/2310.00673/) (2023)
- [Automated Type Annotation in Python Using Large Language Models](https://mlsanity.com/paper/2508.00422/) (2025)

## Abstract (arXiv)

We present a new approach to the type inference problem for dynamic languages. Our goal is to combine logical constraints, that is, deterministic information from a type system, with natural constraints, that is, uncertain statistical information about types learnt from sources like identifier names. To this end, we introduce a framework for probabilistic type inference that combines logic and learning: logical constraints on the types are extracted from the program, and deep learning is applied to predict types from surface-level code properties that are statistically associated. The foremost insight of our method is to constrain the predictions from the learning procedure to respect the logical constraints, which we achieve by relaxing the logical inference problem of type prediction into a continuous optimisation problem. We build a tool called OptTyper to predict missing types for TypeScript files. OptTyper combines a continuous interpretation of logical constraints derived by classical static analysis of TypeScript code, with natural constraints obtained from a deep learning model, which learns naming conventions for types from a large codebase. By evaluating OptTyper, we show that the combination of logical and natural constraints yields a large improvement in performance over either kind of information individually and achieves a 4% improvement over the state-of-the-art.