Products, developers, and milestones: how should I build my N-Gram language model

By Juliana Saraiva, Christian Bird, Thomas Zimmermann. 2015

12 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software, Natural Language Processing, Code Generation & Program Synthesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vincent J. Hellendoorn
2. Premkumar T. Devanbu
3. Miltiadis Allamanis
4. Charles Sutton
5. Rafael-Michael Karampatsis
6. Romain Robbes
7. Andrea Janes
8. Hlib Babii

In Information Systems

1. Vincent J. Hellendoorn
2. Premkumar T. Devanbu
3. Miltiadis Allamanis
4. Charles Sutton
5. Rafael-Michael Karampatsis
6. Earl T. Barr
7. Baishakhi Ray
8. Michael Pradel

In Software

1. Vincent J. Hellendoorn
2. Premkumar T. Devanbu
3. Charles Sutton
4. Miltiadis Allamanis
5. Earl T. Barr
6. Rafael-Michael Karampatsis
7. Tien Nhut Nguyen
8. Anh Tuan Nguyen

In Natural Language Processing

1. Premkumar T. Devanbu
2. Vincent J. Hellendoorn

In Code Generation & Program Synthesis

1. Vincent J. Hellendoorn
2. Premkumar T. Devanbu
3. Miltiadis Allamanis
4. Charles Sutton
5. Rafael-Michael Karampatsis
6. Earl T. Barr
7. Tien Nhut Nguyen
8. Anh Tuan Nguyen

Citations in the corpus

• Are deep neural networks the best choice for modeling source code? (2017, background): “Researchers have even noted applicationand developer-specific vocabularies [35].”