Abstract Static type errors are a common stumbling block for newcomers to typed functional languages. We present a dynamic approach to explaining type errors by generating counterexample witness inputs that illustrate how an ill-typed program goes wrong. First, given an ill-typed function, we symbolically execute the body to synthesize witness values that make the program go wrong. We prove that our procedure synthesizes general witnesses in that if a witness is found, then for all inhabited input types, there exist values that can make the function go wrong. Second, we show how to extend this procedure to produce a reduction graph that can be used to interactively visualize and debug witness executions. Third, we evaluate the coverage of our approach on two data sets comprising over 4,500 ill-typed student programs. Our technique is able to generate witnesses for around 85% of the programs, our reduction graph yields small counterexamples for over 80% of the witnesses, and a simple heuristic allows us to use witnesses to locate the source of type errors with around 70% accuracy. Finally, we evaluate whether our witnesses help students understand and fix type errors, and find that students presented with our witnesses show a greater understanding of type errors than those presented with a standard error message.

Localizing type errors is challenging in languages with global type inference, as the type checker must make assumptions about what the programmer intended to do. We introduce Nate, a data-driven approach to error localization based on supervised learning. Nate analyzes a large corpus of training data -- pairs of ill-typed programs and their "fixed" versions -- to automatically learn a model of where the error is most likely to be found. Given a new ill-typed program, Nate executes the model to generate a list of potential blame assignments ranked by likelihood. We evaluate Nate by comparing its precision to the state of the art on a set of over 5,000 ill-typed OCaml programs drawn from two instances of an introductory programming course. We show that when the top-ranked blame assignment is considered, Nate's data-driven model is able to correctly predict the exact sub-expression that should be changed 72% of the time, 28 points higher than OCaml and 16 points higher than the state-of-the-art SHErrLoc tool. Furthermore, Nate's accuracy surpasses 85% when we consider the top two locations and reaches 91% if we consider the top three.

Static type errors are a common stumbling block for newcomers to typed functional languages. We present a dynamic approach to explaining type errors by generating counterexample witness inputs that illustrate how an ill-typed program goes wrong. First, given an ill-typed function, we symbolically execute the body to synthesize witness values that make the program go wrong. We prove that our procedure synthesizes general witnesses in that if a witness is found, then for all inhabited input types, there exist values that can make the function go wrong. Second, we show how to extend the above procedure to produce a reduction graph that can be used to interactively visualize and debug witness executions. Third, we evaluate the coverage of our approach on two data sets comprising over 4,500 ill-typed student programs. Our technique is able to generate witnesses for 88% of the programs, and our reduction graph yields small counterexamples for 81% of the witnesses. Finally, we evaluate whether our witnesses help students understand and fix type errors, and find that students presented with our witnesses show a greater understanding of type errors than those presented with a standard error message.

Ivory is a language that enforces memory safety and avoids most undefined behaviors while providing low-level control of memory- manipulation. Ivory is embedded in a modern variant of Haskell, as implemented by the GHC compiler. The main contributions of the paper are two-fold. First, we demonstrate how to embed the type-system of a safe-C language into the type extensions of GHC. Second, Ivory is of interest in its own right, as a powerful language for writing high-assurance embedded programs. Beyond invariants enforced by its type-system, Ivory has direct support for model-checking, theorem-proving, and property-based testing. Ivory’s semantics have been formalized and proved to guarantee memory safety.

Haskell has many delightful features. Perhaps the one most beloved by its users is its type system that allows developers to specify and verify a variety of program properties at compile time. However, many properties, typically those that depend on relationships between program values are impossible, or at the very least, cumbersome to encode within the existing type system. Many such properties can be verified using a combination of Refinement Types and external SMT solvers. We describe the refinement type checker liquidHaskell, which we have used to specify and verify a variety of properties of over 10,000 lines of Haskell code from various popular libraries, including containers, hscolour, bytestring, text, vector-algorithms and xmonad. First, we present a high-level overview of liquidHaskell, through a tour of its features. Second, we present a qualitative discussion of the kinds of properties that can be checked -- ranging from generic application independent criteria like totality and termination, to application specific concerns like memory safety and data structure correctness invariants. Finally, we present a quantitative evaluation of the approach, with a view towards measuring the efficiency and programmer effort required for verification, and discuss the limitations of the approach.

SMT-based checking of refinement types for call-by-value languages is a well-studied subject. Unfortunately, the classical translation of refinement types to verification conditions is unsound under lazy evaluation. When checking an expression, such systems implicitly assume that all the free variables in the expression are bound to values. This property is trivially guaranteed by eager, but does not hold under lazy, evaluation. Thus, to be sound and precise, a refinement type system for Haskell and the corresponding verification conditions must take into account which subset of binders actually reduces to values. We present a stratified type system that labels binders as potentially diverging or not, and that (circularly) uses refinement types to verify the labeling. We have implemented our system in LIQUIDHASKELL and present an experimental evaluation of our approach on more than 10,000 lines of widely used Haskell libraries. We show that LIQUIDHASKELL is able to prove 96% of all recursive functions terminating, while requiring a modest 1.7 lines of termination-annotations per 100 lines of code.