88 citations · 110 across the 7 of their papers we have counts for
15 papers
Disproving Program Equivalence with LLMs
Miltiadis Allamanis, Pengcheng Yin
To evaluate large language models (LLMs) for code, research has used manually created unit test-based benchmarks. However, these tests are often inadequate, missing corner cases an…
Is Surprisal in Issue Trackers Actionable?
James Caddy, Markus Wagner, Christoph Treude +2
Background. From information theory, surprisal is a measurement of how unexpected an event is. Statistical language models provide a probabilistic approximation of natural language…
CoRGi: Content-Rich Graph Neural Networks with Attention
Jooyeon Kim, Angus Lamb, Simon Woodhead +3
Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich informat…
Copy that! Editing Sequences by Copying Spans
Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt
Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code. In this paper, we argue t…
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…
Fast and Memory-Efficient Neural Code Completion
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi +3
Code completion is one of the most widely used features of modern integrated development environments (IDEs). While deep learning has made significant progress in the statistical p…