A polymorphic, constraint-based type inference algorithm for an object-oriented language is defined. A generalized form of type, polymorphic recursively constrained types, are inferred. These types are expressive enough for typing objects, since they generalize recursive types and F-bounded polymorphism. The well-known tradeoff between inheritance and subtyping is mitigated by the type inference mechanism. Soundness and completeness of type inference are established. 1 Introduction Type inference, the process of automatically inferring type information from untyped programs, is originally due to Hindley and Milner [16]. These ideas have found their way into some recent innovative programming languages, including Standard ML [17]. The type inference problem for object-oriented languages is a challenging one: even simple object-oriented programs require quite advanced features to be present in the type system. One of the main sources of difficulty lies with binary methods, such as an a...

This paper addresses the problem of designing an object-oriented programming language with an effective type inference mechanism. Recently developed programming languages including Standard ML and Haskell incorporate type inference as a core component of the language. However, type inference has yet to achieve practical application to object-oriented programming languages. We strongly feel the core type features necessary to model object-oriented programming with type inference include a notion of subtyping [CW85], and a notion of "recursively constrained polymorphism," a generalization of F-bounded polymorphism [CHC90, CCH

Constraint-based type inference infers types with subtyping constraints. Such types can capture detailed data and control flow information about the analyzed program. In the presence of polymorphism, existing constraint-based type inference algorithms sacrifice much precision for efficiency. This paper presents both theoretical and practical results on developing precise and efficient polymorphic constraint-based type inference for object-oriented languages. We develop a novel theoretical framework for polymorphic constraint-based type inference. A concrete type inference algorithm can be obtained by instantiating the framework with a particular strategy for handling polymorphism. We define well-known algorithms such as Shivers’ nCFA and Agesen’s Cartesian Product Algorithm (CPA) as instantiations of the framework. We prove the soundness of the framework, which entails the soundness of every algorithm defined as an instantiation of the framework. Using the framework, we develop a novel algorithm, Data Polymorphic CPA (DCPA), which extends Agesen’s CPA Algorithm to achieve high precision in the presence of Data Polymorphism. We further study the construction of practical constraint-based type inference systems for realistic programming languages. We have constructed a type inference system for the full Java language. The system includes implementations of the 0CFA, CPA and DCPA algorithms, and it incorporates a number of novel implementation techniques for achieving scalability. The system is used to statically verify the correctness of Java downcasts. Benchmark results on realistic Java applications show that the DCPA algorithm has good precision and efficiency: it is significantly more accurate than existing algorithms and its efficiency is comparable to CPA.