Can we use machine learning to create better tools for software engineers?
My research concerns machine learning models and methods that "understand"
source code. By learning from existing code, I aim to create
useful machine learning-based software engineering tools, interfaces and insights.
My research focuses on developer tools with a strong machine learning component,
while using problems in this area to motivate
machine learning research.
I am currently a senior researcher at Microsoft Research in Cambridge, UK
and part of the
Deep Program Understanding
Learning to Represent Edits
P. Yin, G. Neubig, M. Allamanis, M. Brockschmidt, A. L. Gaunt. ICLR 2019
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
H. Husain, H. Wu, T. Gazit, M. Allamanis, M. Brockschmidt. 2019
The Adverse Effects of Code Duplication in Machine Learning Models of Code
M. Allamanis. SPLASH Onward! 2019
A Survey of Machine Learning for Big Code and Naturalness
M. Allamanis, E. T. Barr, P. Devanbu, C. Sutton. ACM Computing Surveys 2018
Learning to Represent Programs with Graphs
M. Allamanis, M. Brockscmidt, M. Khademi. ICLR 2018
Full list of publications