Type Inference in Java: Characteristics and Limitations

Certain variants of object-oriented Datalog can be compiled to Datalog with negation. We seek to apply optimisations akin to virtual method resolution (a well-known technique in compiling Java and other OO languages) to improve efficiency of the resulting Datalog programs. The effectiveness of such optimisations strongly depends on the precision of the underlying type inference algorithm. Previous work on type inference for Datalog has focussed on Cartesian abstractions, where the type of each field is computed separately. Such Cartesian type inference is inherently imprecise in the presence of field equalities. We propose a type system where equalities are tracked, and present a type inference algorithm. The algorithm is proved sound. We also prove that it is optimal for Datalog without negation, in the sense that the inferred type is as tight as possible. Extensive experiments with our type-based optimisations, in a commercial implementation of object-oriented Datalog, confirm the benefits of this non-Cartesian type inference algorithm.

The response of compilers to programs with type errors can be unpredictable and confusing. In part this is because the point at which type inference fails may not be the point at which the programmer has made a mistake. This paper explores a way of making type inference algorithms fail at different locations in programs so that clearer error messages may then be produced. Critical to the operation of type inference algorithms is their use of substitutions. We will see that the way in which substitutions are applied in type inference algorithm W means that errors are detected towards the right-hand side of expressions. This paper introduces a new operation -- unification of substitutions -- which allows greater control over the use of substitutions so that this bias can be removed.

For many compiled languages, source-level types are erased very early in the compilation process. As a result, further compiler passes may convert type-safe source into type-unsafe machine code. Type-unsafe idioms in the original source and type-unsafe optimizations mean that type information in a stripped binary is essentially nonexistent. The problem of recovering high-level types by performing type inference over stripped machine code is called type reconstruction, and offers a useful capability in support of reverse engineering and decompilation. In this paper, we motivate and develop a novel type system and algorithm for machine-code type inference. The features of this type system were developed by surveying a wide collection of common source- and machine-code idioms, building a catalog of challenging cases for type reconstruction. We found that these idioms place a sophisticated set of requirements on the type system, inducing features such as recursively-constrained polymorphic types. Many of the features we identify are often seen only in expressive and powerful type systems used by high-level functional languages. Using these type-system features as a guideline, we have developed Retypd: a novel static type-inference algorithm for machine code that supports recursive types, polymorphism, and subtyping. Retypd yields more accurate inferred types than existing algorithms, while also enabling new capabilities such as reconstruction of pointer const annotations with 98% recall. Retypd can operate on weaker program representations than the current state of the art, removing the need for high-quality points-to information that may be impractical to compute.

For many compiled languages, source-level types are erased very early in the compilation process. As a result, further compiler passes may convert type-safe source into type-unsafe machine code. Type-unsafe idioms in the original source and type-unsafe optimizations mean that type information in a stripped binary is essentially nonexistent. The problem of recovering high-level types by performing type inference over stripped machine code is called type reconstruction, and offers a useful capability in support of reverse engineering and decompilation. In this paper, we motivate and develop a novel type system and algorithm for machine-code type inference. The features of this type system were developed by surveying a wide collection of common source- and machine-code idioms, building a catalog of challenging cases for type reconstruction. We found that these idioms place a sophisticated set of requirements on the type system, inducing features such as recursively-constrained polymorphic types. Many of the features we identify are often seen only in expressive and powerful type systems used by high-level functional languages. Using these type-system features as a guideline, we have developed Retypd: a novel static type-inference algorithm for machine code that supports recursive types, polymorphism, and subtyping. Retypd yields more accurate inferred types than existing algorithms, while also enabling new capabilities such as reconstruction of pointer const annotations with 98% recall. Retypd can operate on weaker program representations than the current state of the art, removing the need for high-quality points-to information that may be impractical to compute.

This paper presents a polymorphic type inference algorithm for a small subset of JavaScript. The goal is to prevent accessing undefined members of objects. We define a type system that allows explicit extension of objects through add operation and implicit extension through method calls. The type system also permits strong updates and unrestricted extensions to new objects. The type inference algorithm is modular so that each function definition is only analyzed once and larger programs can be checked incrementally.

The article presents type inference for programs written in Python programming language. At first, type inference algorithms for parametric polymorphism that were described in the scientific literature are reviewed. The algorithms evolved from “basic” algorithm (also known as Palsberg — Schwartzbach algorithm) to Cartesian product algorithm (CPA). It was shown that CPA is both precise and efficient, however it has to be modified to be used on Python code. Afterwards, we present a new algorithm (a modification of CPA) for Python. It is shown how type inference module using the new algorithm analyses various Python language structures: constants (literals), basic Python collections, variable names, assignments, function and class definitions, attribute and index expressions. It is also shown how the algorithm deals with external (non-Python) functions using special annotations that specify output types depending on input types of the function. Afterwards, the results of work on the prototype (module that implements described type inference algorithm) are presented. The paper is concluded by an overview of possible future work directions such as generating a defect trace, i.e. description that specifies how expression got its incorrect types.

A common approach to improve software quality is to use programming guidelines to avoid common kinds of errors. In this paper, we consider the problem of enforcing guidelines for Featherweight Java (FJ). We formalize guidelines as sets of finite or infinite execution traces and develop a region-based type and effect system for FJ that can enforce such guidelines. We build on the work by Erbatur, Hofmann and Z\u{a}linescu, who presented a type system for verifying the finite event traces of terminating FJ programs. We refine this type system, separating region typing from FJ typing, and use ideas of Hofmann and Chen to extend it to capture also infinite traces produced by non-terminating programs. Our type and effect system can express properties of both finite and infinite traces and can compute information about the possible infinite traces of FJ programs. Specifically, the set of infinite traces of a method is constructed as the greatest fixed point of the operator which calculates the possible traces of method bodies. Our type inference algorithm is realized by working with the finitary abstraction of the system based on B\"uchi automata.

LHD 2.0: A text mining approach to typing entities in knowledge graphs

The paper describes the refinement algorithm for the Calculus of (Co)Inductive Constructions (CIC) implemented in the interactive theorem prover Matita. The refinement algorithm is in charge of giving a meaning to the terms, types and proof terms directly written by the user or generated by using tactics, decision procedures or general automation. The terms are written in an "external syntax" meant to be user friendly that allows omission of information, untyped binders and a certain liberal use of user defined sub-typing. The refiner modifies the terms to obtain related well typed terms in the internal syntax understood by the kernel of the ITP. In particular, it acts as a type inference algorithm when all the binders are untyped. The proposed algorithm is bi-directional: given a term in external syntax and a type expected for the term, it propagates as much typing information as possible towards the leaves of the term. Traditional mono-directional algorithms, instead, proceed in a bottom-up way by inferring the type of a sub-term and comparing (unifying) it with the type expected by its context only at the end. We propose some novel bi-directional rules for CIC that are particularly effective. Among the benefits of bi-directionality we have better error message reporting and better inference of dependent types. Moreover, thanks to bi-directionality, the coercion system for sub-typing is more effective and type inference generates simpler unification problems that are more likely to be solved by the inherently incomplete higher order unification algorithms implemented. Finally we introduce in the external syntax the notion of vector of placeholders that enables to omit at once an arbitrary number of arguments. Vectors of placeholders allow a trivial implementation of implicit arguments and greatly simplify the implementation of primitive and simple tactics.

Hindley and Milner's type system, in its purest form, allows what one might call full type inference. Even when a program contains no explicit type information, its very structure gives rise to a conjunction of type equations (or, more generally, to a constraint) whose resolution allows reconstructing the type of every variable and of every sub-expression. Actual programming languages based on this type system, such as Standard ML, Objective Caml, and Haskell, do allow users to provide explicit type information via optional type annotations. Yet, this extra information does not make type inference any easier: it simply gives rise to extra equations, leading to a more specific constraint.There are extensions of Hindley and Milner's type system where full type inference becomes undecidable or intractable. Then, it is common to require explicit type information, via mandatory type annotations. Polymorphic recursion is a simple and well-known instance of this phenomenon. This talk presents two more elaborate instances, commonly known as arbitrary-rank polymorphism and generalized algebraic data types.In both cases, type inference is made tractable thanks to mandatory type annotations. There is a twist, though: these required annotations are often numerous and redundant. So, it seems desirable to make them optional again, and to set up a distinct mechanism to guess some of the elided annotations. This mechanism, referred to as elaboration, can be heuristic. It must be predictable. In the two cases considered in this talk, it is a form of local type inference.This approach leads to a so-called stratified type inference system, where type information is propagated first in a local, ad hoc manner during elaboration, then in a possibly nonlocal, but well-specified manner during constraint solving. This appears to be one of the less displeasing ways of describing type inference systems that are able to exploit optional type annotations to infer better (or different) types.

A compiler-checked immutability guarantee provides useful documentation, facilitates reasoning, and enables optimizations. This paper presents Immutability Generic Java (IGJ), a novel language extension that expresses immutability without changing Java's syntax by building upon Java's generics and annotation mechanisms. In IGJ, each class has one additional type parameter that is Immutable, Mutable, or ReadOnly. IGJ guarantees both reference immutability (only mutable references can mutate an object) and object immutability (an immutable reference points to an immutable object). IGJ is the first proposal for enforcing object immutability within Java's syntax and type system, and its reference immutability is more expressive than previous work. IGJ also permits covariant changes of type parameters in a type-safe manner, e.g., a readonly list of integers is a subtype of a readonly list of numbers. IGJ extends Java's type system with a few simple rules. We formalize this type system and prove it sound. Our IGJ compiler works by type-erasure and generates byte-code that can be executed on any JVM without runtime penalty.

The S and K combinator expressions form a well-known Turing-complete subset of the lambda calculus. Using Prolog as a meta-language, we specify evaluation, type inference and combinatorial generation algorithms for SK-combinator trees. In the process, we unravel properties shedding new light on interesting aspects of their structure and distribution. We study the proportion of well-typed terms among size-limited SK-expressions as well as the type-directed generation of terms of sizes smaller than the size of their simple types. We also introduce the well-typed frontier of an untypable term and we use it to design a simplification algorithm for untypable terms taking advantage of the fact that well-typed terms are normalizable.

Object-oriented scripting languages like Javascript and Python are popular partly because of their dynamic features. These include the runtime modification of objects and classes through addition of fields or updating of methods. These features make static typing difficult and so usually dynamic typing is used. Consequently, errors such as access to non-existent members are not detected until runtime.We first develop a formalism for an object based language, JS0 with features from Javascript, including dynamic addition of fields and updating of methods. We give an operational semantics and static type system for JS0using structural types. Our types allow objects to evolve in a controlled manner by classifying members as definite or potential.We define a type inference algorithm for JS0 that is sound with respect to the type system. If the type inference algorithm succeeds, then the program is typeable. Therefore, programmers can benefit from the safety offered by the type system, without the need to write explicitly types in their programs.

A mixin module is a programming abstraction that simultaneously generalizes λ-abstractions, records, and mutually recursive definitions. Although various mixin module type systems have been developed, no one has investigated principal typings or developed type inference for first-class mixin modules, nor has anyone added Milner's let-polymorphism to such a system.This paper proves that typability is NP-complete for the naive approach followed by previous mixin module type systems. Because a λ-calculus extended with record concatenation is a simple restriction of our mixin module calculus, we also prove the folk belief that typability is NP-complete for the naive early type systems for record concatenation.To allow feasible type inference, we present Martini, a new system of simple types for mixin modules with principal typings . Martini is conceptually simple, with no subtyping and a clean and balanced separation between unification-based type inference with type and row variables and constraint solving for safety of linking and field extraction. We have implemented a type inference algorithm and we prove its complexity to be O ( n 2 ), or O ( n ) given a fixed bound on the number of field labels. To prove the complexity, we need to present an algorithm for row unification that may have been implemented by others, but which we could not find written down anywhere. Because Martini has principal typings, we successfully extend it with Milner's let-polymorphism.

This paper shows how type effect systems can be combined with model-checking techniques to produce powerful, automatically verifiable program logics for higher order programs. The properties verified are based on the ordered sequence of events that occur during program execution, so-called event traces . Our type and effect systems infer conservative approximations of the event traces arising at run-time, and model-checking techniques are used to verify logical properties of these histories. Our language model is based on the λ-calculus. Technical results include a type inference algorithm for a polymorphic type effect system, and a method for applying known model-checking techniques to the trace effects inferred by the type inference algorithm, allowing static enforcement of history- and stack-based security mechanisms. A type safety result is proven for both unification and subtyping constraint versions of the type system, ensuring that statically well-typed programs do not contain trace event checks that can fail at run-time.