OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
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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, such as variable names. 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. As proof of concept, we build a tool called OptTyper to predict missing types for TypeScript files. OptTyper combines a continuous interpretation of logical constraints derived by a simple program transformation and 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 3
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