OppropBERL: A GNN and BERT-Style Reinforcement Learning-Based Type Inference
Bibliographic record
Abstract
Main-stream type systems do not prevent errors such as null-pointer exceptions, security problems, and con-currency errors. Optional Properties (Opprop) or pluggable type systems provide frameworks where users can guarantee a particular property holds with the help of a customizable type checker. Type annotations are used to specify a property, e.g., whether a reference can be null or not, and custom type rules enforce that property. However, manually inserting these type annotations for new and existing large projects requires a lot of human effort. Inference systems provide a constraint-based whole-program inference framework. However, thoroughly understanding the underlying framework to develop such a system is time-consuming. Furthermore, these frameworks make expensive calls to SAT and SMT solvers, which increases the runtime overhead during inference. Type system developers write test cases to ensure their type checker covers all the necessary type rules and works as expected. Our core idea is to leverage these manually written test cases along with the type checker to create a Deep Learning model to learn the type rules implicitly using a data-driven approach to automatically infer annotations for programs. We present a novel model, OppropBERL, which takes as input a Java program to predict the appropriate type annotations for a given type system. The pre-trained Transformer model helps encode the code tokens without specifying the programming language's grammar, including the type rules. In the presence of a type checker, the model can be refined further using a reinforcement learning (RL) technique. With comprehensive ex-periments, we establish the efficacy of OppropBERL for null ness and ownership annotation prediction tasks by comparing against 8 different tools on publicly available Java projects with around 240K lines of code.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".