Tunable Static Inference for Generic Universe Types
Object ownership is useful for many applications, including program verification, thread synchronization, and memory management. However, the annotation overhead of ownership type systems hampers their widespread application. This paper addresses this issue by presenting a tunable static type inference for Generic Universe Types. In contrast to classical type systems, ownership types have no single most general typing. The authors' inference chooses among the legal typings via heuristics. Their inference is tunable: users can indicate a preference for certain typings by adjusting the heuristics or by supplying partial annotations for the program.