Finding minimum type error sources

TL;DR: GuiLT as mentioned in this paper is an interactive tool for gradual liquid type inference in Liquid Haskell that both infers modular types and explores safe concretizations of gradual refinements, i.e., the static refinements that justify why a program is gradually well-typed.

TL;DR: In this paper, a study of 3 program data sets, which were written in different years, using different compilers, and were of diverse sizes, was conducted to investigate the debugging behaviors of students in functional programming, which could be exploited for developing effective error debuggers.

TL;DR: L Learnskell is a type error debugger that uses machine learning to help diagnose and deliver high quality error messages, for programs that contain nonstructural errors, and it is found that it scales to large programs.

TL;DR: A new error-correction algorithm for functional languages is presented, which combines statistical error-localization and type-directed program synthesis enhanced with components reduction and search space pruning using symbolic execution.

TL;DR: Checking the satisfiability of logical formulas, SMT solvers scale orders of magnitude beyond custom ad hoc solvers.

TL;DR: This work provides a modular SAT solver architecture designed to work with generic constraints that is used to provide SAT, MaxSat and pseudo-boolean and solvers for lightweight constraint programming.

TL;DR: A technique that synthesizes and suggests valid expressions of a given type at a given program point and introduces a succinct representation for type judgements that merges types into equivalence classes to reduce the search space, then reconstructs any desired number of solutions on demand.

TL;DR: An algorithm is described which appears to be quite helpful in isolating and explaining the source of type errors by keeping track of the reasons the type-checker makes deductions about the types of variables.