Improving Type Error Messages in OCaml

International audienceCryptic 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

Debugging Type Errors with a Blackbox Compiler

Type error debugging can be a laborious yet necessary process for programmers of statically typed functional programming languages. Often a compiler compounds this by inaccurately reporting the location of a type error, a problem that has been a subject of research for over thirty years. However, despite its long history, the solutions proposed are often reliant on direct modifications to the compiler, often distributed in the form of patches. These patches append another level of arduous activity to the task of debugging, keeping them modernised to the ever-changing programming language they support. This thesis investigates an additional option; the blackbox compiler. Split into three central parts, it shows the individual solutions involved in using a blackbox compiler to debug type errors in functional programming languages. First is a demonstration of how the combination of a blackbox compiler and a generic debugging algorithm can successfully locate type errors. Next tackled is a side-effect of this new combination, the introduction of extra errors, combated with a new speed boosted algorithm, evaluated with a proposed framework based on Data Science techniques to quantify the quality of a type error debugger. Lastly, the algorithms employed throughout this thesis, along with the blackbox compiler, have agnostic properties, they do not need language-specific knowledge. Thus, the final part presents utilising the agnostic abilities for an agnostic debugger to locate type errors