88 citations · 90 across the 4 of their papers we have counts for
4 papers · 1 filter
Typilus: Neural Type Hints
Miltiadis Allamanis, Earl T. Barr, Soline Ducousso +1
Type inference over partial contexts in dynamically typed languages is challenging. In this work, we present a graph neural network model that predicts types by probabilistically r…
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon +1
We present a new approach to the type inference problem for dynamic languages. Our goal is to combine \emph{logical} constraints, that is, deterministic information from a type sys…
Sub-Turing Islands in the Wild
Earl T. Barr, David W. Binkley, Mark Harman +1
Recently, there has been growing debate as to whether or not static analysis can be truly sound. In spite of this concern, research on techniques seeking to at least partially answ…
SafeStrings: Representing Strings as Structured Data
David Kelly, Mark Marron, David Clark +1
Strings are ubiquitous in code. Not all strings are created equal, some contain structure that makes them incompatible with other strings. CSS units are an obvious example. Worse,…