Towards a universal code formatter through machine learning
By Terence John Parr, Jurgen J. Vinju. 2016
30 citations recorded by the source corpus.
Subfields: Information Systems, Software, Code Generation & Program Synthesis, Program Analysis & Verification, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Michael Pradel
- Rishabh Singh
- Rafael-Michael Karampatsis
- Eran Yahav
In Software
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Michael Pradel
- Rishabh Singh
- Marc Brockschmidt
- Sahil Bhatia
In Code Generation & Program Synthesis
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Han Liu
- Rishabh Singh
- Michael Pradel
- Rafael-Michael Karampatsis
In Program Analysis & Verification
- Miltiadis Allamanis
- Charles Sutton
- Earl T. Barr
- Premkumar T. Devanbu
- Eran Yahav
- Uri Alon
- Marc Brockschmidt
- Omer Levy
In Signal Processing
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Eran Yahav
- Uri Alon
- Prem Devanbu
- Michael Pradel
Citations in the corpus
- A Survey of Machine Learning for Big Code and Naturalness (2017, methodology): “To handle code formatting conventions, Parr and Vinju [152] learn a source code formatter from data by using a set of hand-crafted features from the AST and a k-NN classifier.”