Publications

Below you can find a list of my publications, ordered chronologically.

2019.0

The Adverse Effects of Code Duplication in Machine Learning Models of Code.
M. Allamanis. SPLASH Onward! 2019.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.
H. Husain, H. Wu, T. Gazit, M. Allamanis, M. Brockschmidt. 2019.
CODIT: Code Editing with Tree-Based Neural Machine Translation.
S. Chakraborty, M. Allamanis, B. Ray. 2019.
Generative Code Modeling with Graphs.
M. Brockschmidt, M. Allamanis, A. L. Gaunt, O. Polozov. ICLR 2019.
Learning units-of-measure from scientific code.
M. Danish, M. Allamanis, M. Brockschmidt, A. Rice, D. Orchard. SE 4 Science Workshop 2019.
Learning to Represent Edits.
P. Yin, G. Neubig, M. Allamanis, M. Brockschmidt, A. L. Gaunt. ICLR 2019.
A Neural Approach to Decompiled Identifier Renaming.
J. Lacomis, P. Yin, E.J. Schwartz, M. Allamanis, C. Le Goues, G. Neubig, B. Vasilescu. ASE 2019.
Program Synthesis and Semantic Parsing with Learned Code Idioms.
R. Shin, M. Allamanis, M. Brockschmidt, O. Polozov. NeurIPS 2019.
Structured Neural Summarization.
P. Fernandes, M. Allamanis, M. Brockschmidt. ICLR 2019.

2018.0

Constrained Graph Variational Autoencoders for Molecule Design.
Q. Liu, M. Allamanis, M. Brockschmidt, A. L. Gaunt. NIPS 2018.
Deep Learning Type Inference.
V. Hellendoorn, C. Bird, E. T. Barr, M. Allamanis. FSE 2018.
Learning to Represent Programs with Graphs.
M. Allamanis, M. Brockscmidt, M. Khademi. ICLR 2018.
Mining Semantic Loop Idioms from Big Code.
M. Allamanis, E. T. Barr, C. Bird, M. Marron, C. Sutton. IEEE Transactions in Software Engineering 2018.
RefiNym: Using Names to Refine Types.
S. Dash, M. Allamanis, E. T. Barr. FSE 2018.
A Survey of Machine Learning for Big Code and Naturalness.
M. Allamanis, E. T. Barr, P. Devanbu, C. Sutton. ACM Computing Surveys 2018.

2017.0

Autofolding for Source Code Summarization.
J. Fowkes, P. Chanthirasegaran, R. Ranca, M. Allamanis, M. Lapata, C. Sutton. IEEE Transactions on Software Engineering 2017.
Learning Natural Coding Conventions.
M. Allamanis. PhD Dissertation 2017.
Learning Continuous Semantic Representations of Symbolic Expressions.
M. Allamanis, P. Chanthirasegaran, P. Kohli, C. Sutton. ICML 2017.
SmartPaste: Learning to Adapt Source Code.
M. Allamanis, M. Brockscmidt. 2017.

2016.0

A Convolutional Attention Network for Extreme Summarization of Source Code.
M. Allamanis, H. Peng, C. Sutton. ICML 2016.

2015.0

A Bimodal Modelling of Source Code and Natural Language.
M. Allamanis, D. Tarlow, A. D. Gordon, Y. Wei. ICML 2015.
Suggesting Accurate Method and Class Names.
M. Allamanis, E. T. Barr, C. Bird, C. Sutton. FSE 2015.

2014.0

Learning Natural Coding Conventions.
M. Allamanis, E. T. Barr, C. Bird, C. Sutton. FSE 2014.
Mining Idioms from Source Code.
M. Allamanis, C. Sutton. FSE 2014.

2013.0

Mining Source Code Repositories at Massive Scale Using Language Modeling .
M. Allamanis, C. Sutton. MSR 2013.
Why, When, and What: Analyzing Stack Overflow Questions by Topic, Type, and Code.
M. Allamanis, C. Sutton. MSR 2013.

2012.0

Evolution of a Location-based Online Social Network: Analysis and Models.
M. Allamanis, S. Scellato, C. Mascolo. IMC 2012.