| A survey of machine learning for big code and naturalness M Allamanis, ET Barr, P Devanbu, C Sutton ACM Computing Surveys (CSUR) 51 (4), 2018 | 457 | 2018 |
| Learning to represent programs with graphs M Allamanis, M Brockschmidt, M Khademi International Conference on Learning Representations, 2018 | 397 | 2018 |
| A convolutional attention network for extreme summarization of source code M Allamanis, H Peng, C Sutton International conference on machine learning, 2091-2100, 2016 | 380 | 2016 |
| Suggesting accurate method and class names M Allamanis, ET Barr, C Bird, C Sutton Proceedings of the 2015 10th Joint Meeting on Foundations of Software …, 2015 | 322 | 2015 |
| Learning natural coding conventions M Allamanis, ET Barr, C Bird, C Sutton Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations …, 2014 | 321 | 2014 |
| Mining source code repositories at massive scale using language modeling M Allamanis, C Sutton 2013 10th Working Conference on Mining Software Repositories (MSR), 207-216, 2013 | 287 | 2013 |
| Constrained graph variational autoencoders for molecule design Q Liu, M Allamanis, M Brockschmidt, AL Gaunt arXiv preprint arXiv:1805.09076, 2018 | 218 | 2018 |
| Bimodal modelling of source code and natural language M Allamanis, D Tarlow, A Gordon, Y Wei International conference on machine learning, 2123-2132, 2015 | 169 | 2015 |
| Mining idioms from source code M Allamanis, C Sutton Proceedings of the 22nd acm sigsoft international symposium on foundations …, 2014 | 159 | 2014 |
| Why, when, and what: analyzing stack overflow questions by topic, type, and code M Allamanis, C Sutton 2013 10th Working Conference on Mining Software Repositories (MSR), 53-56, 2013 | 139 | 2013 |
| CodeSearchNet challenge: Evaluating the state of semantic code search H Husain, HH Wu, T Gazit, M Allamanis, M Brockschmidt arXiv preprint arXiv:1909.09436, 2019 | 113 | 2019 |
| Evolution of a Location-based Online Social Network: Analysis and Models M Allamanis, S Scellato, C Mascolo Proceedings of ACM Internet Measurement Conference (IMC 2012), 2012 | 107 | 2012 |
| Deep learning type inference VJ Hellendoorn, C Bird, ET Barr, M Allamanis Proceedings of the 2018 26th ACM joint meeting on european software …, 2018 | 103 | 2018 |
| Structured Neural Summarization P Fernandes, M Allamanis, M Brockschmidt ICLR, 2018 | 100 | 2018 |
| The adverse effects of code duplication in machine learning models of code M Allamanis Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas …, 2019 | 89 | 2019 |
| Generative code modeling with graphs M Brockschmidt, M Allamanis, AL Gaunt, O Polozov arXiv preprint arXiv:1805.08490, 2018 | 85 | 2018 |
| Learning continuous semantic representations of symbolic expressions M Allamanis, P Chanthirasegaran, P Kohli, C Sutton International Conference on Machine Learning, 80-88, 2017 | 67 | 2017 |
| Learning to represent edits P Yin, G Neubig, M Allamanis, M Brockschmidt, AL Gaunt International Conference in Representation Learning (ICLR), 2019 | 51 | 2019 |
| Autofolding for source code summarization J Fowkes, P Chanthirasegaran, R Ranca, M Allamanis, M Lapata, ... IEEE Transactions on Software Engineering 43 (12), 1095-1109, 2017 | 50 | 2017 |
| DIRE: A neural approach to decompiled identifier naming J Lacomis, P Yin, E Schwartz, M Allamanis, C Le Goues, G Neubig, ... 2019 34th IEEE/ACM International Conference on Automated Software …, 2019 | 38 | 2019 |
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
