NullAway delivers practical compile-time null safety for Java with 1.15x build overhead and zero NPEs from its unsound assumptions in evaluated production Android crash data.
ICSE '09: Proceedings of the 31st International Conference on Software Engineering , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
HyPOLE introduces a HyperLTL-guided framework for partial-observability MARL integrated with CTDE, claiming advantages over baselines on SMAC, MessySMAC, and WildFire.
citing papers explorer
-
NullAway: Practical Type-Based Null Safety for Java
NullAway delivers practical compile-time null safety for Java with 1.15x build overhead and zero NPEs from its unsound assumptions in evaluated production Android crash data.
-
HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation
HyPOLE introduces a HyperLTL-guided framework for partial-observability MARL integrated with CTDE, claiming advantages over baselines on SMAC, MessySMAC, and WildFire.