Inferring Pluggable Types with Machine Learning
This addresses the deployment challenge of pluggable type systems for programmers working with legacy code, representing an incremental improvement through a novel machine learning approach.
The paper tackled the problem of manually annotating type qualifiers in legacy codebases for pluggable type systems by using machine learning to infer them automatically, achieving a recall of 0.89 and precision of 0.6 with a Graph Transformer Network model.
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.