Improving Type Error Messages in OCaml

Abstract

Cryptic type error messages are a major obstacle to learning OCaml or other ML-based languages. In many cases, error messages cannot be interpreted without a sufficiently-precise model of the type inference algorithm. The problem of improving type error messages in ML has received quite a bit of attention over the past two decades, and many different strategies have been considered. The challenge is not only to produce error messages that are both sufficiently concise and systematically useful to the programmer, but also to handle a full-blown programming language and to cope with large-sized programs efficiently.In this work, we present a modification to the traditional ML type inference algorithm implemented in OCaml that, by significantly reducing the left-to-right bias, allows us to report error messages that are more helpful to the programmer. Our algorithm remains fully predictable and continues to produce fairly concise error messages that always help making some progress towards fixing the code. We implemented our approach as a patch to the OCaml compiler in just a few hundred lines of code. We believe that this patch should benefit not just to beginners, but also to experienced programs developing large-scale OCaml programs.

Type errors for the IDE with Xtext and Xsemantics

“…The crucial problem with such DSLs is that possible type errors usually leak details of the DSL implementation and are less informative for the user [30]. For this reason, several mechanisms have been proposed for functional languages to customize the type errors generated by the compiler [31][32][33][34][35][36][37], in order to make such errors expressed in terms of the domain.…”

Dynamic witnesses for static type errors (or, Ill-Typed Programs Usually Go Wrong)

“…Hage & Heeren (2006) identify a variety of general heuristics to improve the quality of type error messages, based on their teaching experience. Charguéraud (2014) presents a tabular format for type errors that can provide multiple explanations in a compact form. Heeren et al (2003), Christiansen (2014), andSerrano &Hage (2016) provide methods for library authors to specialize type errors with domain-specific knowledge.…”