There is significant room for improving users' experiences with model checking tools. An error trace produced by a model checker can be lengthy and is indicative of a symptom of an error. As a result, users can spend considerable time examining an error trace in order to understand the cause of the error. Moreover, even state-of-the-art model checkers provide an experience akin to that provided by parsers before syntactic error recovery was invented: they report a single error trace per run. The user has to fix the error and run the model checker again to find more error traces.

Note: This document accompanies the paper “Practical type inference for arbitrary-rank types ” [6]. Prior reading of the main paper is required. 1 Contents

Generic Haskell is an extension of Haskell that supports the construction of generic programs. During the development of several applications, such as an XML editor and compressor, we encountered a number of limitations with the existing (Classic) Generic Haskell language, as implemented by the current Generic Haskell compiler. Specifically,

Previous methods have generally identified the location of a type error as a particular program point or the program subtree rooted at that point. We present a new approach that identifies the location of a type error as a set of program points (a slice) all of which are necessary for the type error. We identify the criteria of completeness and minimality for type error slices. We discuss the advantages of complete and minimal type error slices over previous methods of presenting type errors. We present and prove the correctness of algorithms for finding complete and minimal type error slices for implicitly typed higher-order languages like Standard ML.

To improve the quality of type error messages in functional programming languages, we propose four techniques which influence the behaviour of constraint-based type inference processes. These techniques take the form of externally supplied type inference directives, precluding the need to make any changes to the compiler. A second advantage is that the directives are automatically checked for soundness with respect to the underlying type system. We show how the techniques can be used to improve the type error messages reported for a combinator library. More specifically, how they can help to generate error messages which are conceptually closer to the domain for which the library was developed. The techniques have all been incorporated in the Helium compiler, which implements a large subset of Haskell.

We introduce a new transformation method to eliminate intermediate data structures occurring in functional programs due to repeated list concatenations and other data manipulations (additionally exemplified with list reversal and mapping of functions over lists). The general idea is to uniformly abstract from data constructors and manipulating operations by means of rank-2 polymorphic combinators that exploit algebraic properties of these operations to provide an optimized implementation. The correctness of transformations is proved by using the free theorems derivable from parametric polymorphic types.

Type systems and model checking are two prevalent approaches to program verification. A prominent difference between them is that type systems are typically defined in a syntactic and modular style whereas model checking is usually performed in a semantic and whole-program style. This difference between the two approaches lends them complementary to each other: type systems are good at explaining why a program was accepted while model checkers are good at explaining why a program was rejected. We present a type system that is equivalent to a model checker for verifying temporal safety properties of imperative programs. The model checker is natural and may be instantiated with any finite-state abstraction scheme such as predicate abstraction. The type system, which is also parametric, type checks exactly those programs that are accepted by the model checker. It uses function types to capture flow sensitivity and intersection and union types to capture context sensitivity. Our result sheds light on the relationship between the two approaches, provides a methodology for studying their relative expressiveness, is a step towards sharing results between them, and motivates synergistic program analyses involving interplay between them.

This chapter introduces a novel system for generating type error messages which suggest ways of repairing mistakes. Both the theory behind this, and the implementation (as part of the MLj compiler) are described.

We propose a type system for locating the source of type errors in an applied lambda calculus with ML-style polymorphism. The system is based on discriminative sum types---known from work on soft typing---with annotation subtyping and recursive types. This way, type clashes can be registered in the type for later reporting. The annotations track the potential producers and consumers for each value so that clashes can be traced to their cause.

Abstract. Herbrand constraint solving or unification has long been understood as an efficient mechanism for type checking and inference for programs using Hindley/Milner types. If we step back from the particular solving mechanisms used for Hindley/Milner types, and understand type operations in terms of constraints we not only give a basis for handling Hindley/Milner extensions, but also gain insight into type reasoning even on pure Hindley/Milner types, particularly for type errors. In this paper we consider typing problems as constraint problems and show which constraint algorithms are required to support various typing questions. We use a light weight constraint reasoning formalism, Constraint Handling Rules, to generate suitable algorithms for many popular extensions to Hindley/Milner types. The algorithms we discuss are all implemented as part of the freely available Chameleon system. 1