Pattern Mining Models

Pattern mining models infer, without supervision, a likely latent structure within code. These models are an instantiation of clustering in the code domain; they can find reusable and human-interpretable patterns.
M. Allamanis, C. Sutton, 2014. Mining Idioms from Source Code Graphical Model Syntax ---
M. Allamanis, E. T. Barr, C. Bird, M. Marron, C. Sutton, 2017. Mining Semantic Loop Idioms from Big Code Graphical Model Abstracted AST Semantic Idiom Mining
J. Fowkes, C. Sutton, 2016. Parameter-Free Probabilistic API Mining across GitHub Graphical Model API Call Sequences API Mining
J. Fowkes, R. Ranca, M. Allamanis, M. Lapata, C. Sutton, 2017. Autofolding for Source Code Summarization Graphical Model Tokens Code Summarization
D. Movshovitz-Attias, W. W. Cohen, 2015. KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts Graphical Model Tokens Knowledge-Base Mining
V. Murali, S. Chaudhuri, C. Jermaine, 2017. Bayesian Sketch Learning for Program Synthesis Graphical Model Sketch Synthesis Sketch Mining
V. Murali, S. Chaudhuri, C. Jermaine, 2017. Finding Likely Errors with Bayesian Specifications Graphical Model API Usage Errors Defect Prediction
T.D. Nguyen, A.T. Nguyen, H.D. Phan, T.N. Nguyen, 2017. Exploring API Embedding for API Usages and Applications Distributed API Usage API Mining
S. Wang, T. Liu, L. Tan, 2016. Automatically Learning Semantic Features for Defect Prediction Distributed Serialized ASTs Defect Prediction
M. White, M. Tufano, C. Vendome, D. Poshyvanyk, 2016. Deep Learning Code Fragments for Code Clone Detection Distributed Token + Syntax Clone Detection