The growing cost of deep learning for source code

By Vincent J. Hellendoorn, Anand Ashok Sawant. 2021

21 citations recorded by the source corpus.

Attempting to mitigate problems associated with the trend toward massive dataset scaling.

Subfields: Artificial Intelligence, Information Systems, Code Generation & Program Synthesis, Economics & Operations Research

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. Graham Neubig
2. Uri Alon
3. Frank F. Xu
4. Vincent J. Hellendoorn
5. Felix Stahlberg
6. Hinrich Schütze
7. Luke S. Zettlemoyer
8. Miltiadis Allamanis

In Information Systems

1. Graham Neubig
2. Uri Alon
3. Frank F. Xu
4. Vincent J. Hellendoorn
5. Miltiadis Allamanis
6. Charles Sutton
7. Rafael-Michael Karampatsis
8. Premkumar T. Devanbu

In Code Generation & Program Synthesis

1. Frank F. Xu
2. Graham Neubig
3. Uri Alon
4. Vincent J. Hellendoorn
5. Miltiadis Allamanis
6. Charles Sutton
7. Rafael-Michael Karampatsis
8. Premkumar T. Devanbu

In Economics & Operations Research

1. Graham Neubig
2. Frank F. Xu
3. Uri Alon
4. Vincent J. Hellendoorn

Citations in the corpus

• A systematic evaluation of large language models of code (2022, methodology): “…models, we encourage the community to study and release medium-large scale language models for code, in response to the concerns expressed by Hellendoorn & Sawant (2021): [...] this exploding trend in cost to achieve the state of the art has left the ability to train and test such models…”