Slice-Guided, Context-Augmented Large Language Model Inference of Java Nullability Annotations
TL;DR
A warning-guided, slice-based, LLM-assisted pipeline for inferring Java nullability annotations, which infers the annotations @Nullable and @Nonnull without touching program logic as a step toward a type-system-independent inference technique.
Abstract
Java nullness checkers such as NullAway catch potential null-pointer errors, but adopting them on legacy code is difficult. Most projects lack complete nullability annotations, and existing tools such as NullAwayAnnotator are checker- specific and can still leave warnings behind. We present a warning-guided, slice-based, LLM-assisted pipeline for inferring Java nullability annotations. It first runs NullAway to locate the warnings, then uses Specimin, a type-directed slicer that extracts the minimum compilable code needed to reproduce a warning, to build a small slice around each warning-relevant method or field. It augments the prompt with usage context. The model gets two complementary inputs, the compilable slice and this textual evidence, and infers the annotations @Nullable and @Nonnull without touching program logic. Annotations are merged back, re-checked, and a post-processing loop fixes whatever still triggers a warning. On EventBus, where NullAwayAnnotator leaves 11 warnings unresolved, our pipeline resolves all warnings. We see this checker-guided design as a step toward a type-system-independent inference technique.