Precise Constraint-Based Type Inference for Java
نویسندگان
چکیده
Precise type information is invaluable for analysis and optimization of object-oriented programs. Some forms of polymorphism found in object-oriented languages pose significant difficulty for type inference, in particular data polymorphism. Agesen’s Cartesian Product Algorithm (CPA) can analyze programs with parametric polymorphism in a reasonably precise and efficient manner, but CPA loses precision for programs with data polymorphism. This paper presents a precise constraintbased type inference system for Java. It uses Data-Polymorphic CPA (DCPA), a novel constraint-based type inference algorithm which extends CPA with the ability to accurately and efficiently analyze data polymorphic programs. The system is implemented for the full Java language, and is used to statically verify the correctness of Java downcasts. Benchmark results are given which show that DCPA is significantly more accurate than CPA and the efficiency of DCPA is close to CPA.
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