Source Code-Based Recommendation Systems

By K. Mens, A. Lozano. 2014

25 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software, Natural Language Processing

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. Miltiadis Allamanis
2. Charles Sutton
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Eran Yahav
6. Uri Alon
7. Rishabh Singh
8. Rafael-Michael Karampatsis

In Information Systems

1. Charles Sutton
2. Miltiadis Allamanis
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Michael Pradel
6. Rishabh Singh
7. Rafael-Michael Karampatsis
8. Eran Yahav

In Software

1. Charles Sutton
2. Miltiadis Allamanis
3. Earl T. Barr
4. Premkumar T. Devanbu
5. Michael Pradel
6. Rishabh Singh
7. Marc Brockschmidt
8. Sahil Bhatia

In Natural Language Processing

1. Charles Sutton
2. Premkumar T. Devanbu
3. Miltiadis Allamanis
4. Earl T. Barr
5. Michael Pradel
6. Rishabh Singh
7. Rafael-Michael Karampatsis
8. Marc Brockschmidt

Citations in the corpus

• A Survey of Machine Learning for Big Code and Naturalness (2017, background): “Many of these systems employ data mining and machine learning approaches on various software engineering artifacts. Probabilistic models of code find application in source code-basedrecommendersystems [130], such as those that aid developers write or maintain code. Modeling developer intent is a chal”