Fetching the paper…
Reading the bibliography…
Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code.
Towards a theory of the cognitive processes in computer programming
R. Brooks · 1977
Earlier work this paper cites.
Studying programmer behavior experimentally: The problems of proper methodology
R. E. Brooks · 1980
Earlier work this paper cites.
The keystroke-level model for user performance time with interactive systems
S. K. Card, T. P. Moran, and A. Newell · 1980
Earlier work this paper cites.
The psychological study of programming
B. A. Sheil · 1981
Earlier work this paper cites.
The entropy of markov trajectories
L. Ekroot and T. M. Cover · 1993
Earlier work this paper cites.
Bridging the gulf between code and behavior in programming
H. Lieberman and C. Fry · 1995
Earlier work this paper cites.
The goms family of user interface analysis techniques: Comparison and contrast
B. E. John and D. E. Kieras · 1996
Earlier work this paper cites.
User behavior patterns in the course of programming in c++
Z. Velart and P. Šaloun · 2006
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Teamscope: measuring software engineering processes with teamwork telemetry
A. Ju and A. Fox · 2018
Earlier work this paper cites.
A survey on the usage of eye-tracking in computer programming
U. Obaidellah, M. Al Haek, and P. C.-H. Cheng · 2018
Earlier work this paper cites.
When code completion fails: A case study on real-world completions
V. J. Hellendoorn, S. Proksch, H. C. Gall, and A. Bacchelli · 2019
Earlier work this paper cites.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Earlier work this paper cites.
What drives the reading order of programmers? an eye tracking study
N. Peitek, J. Siegmund, and S. Apel · 2020
Cited alongside, same era.
A survey on semi-supervised learning
J. E. Van Engelen and H. H. Hoos · 2020
Cited alongside, same era.
Evaluating large language models trained on code
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al · 2021
Cited alongside, same era.
Measuring coding challenge competence with apps
D. Hendrycks, S. Basart, S. Kadavath, M. Mazeika, A. Arora, E. Guo, C. Burns, S. Puranik, H. He, D. Song, et al · 2021
Cited alongside, same era.
Can openai codex and other large language models help us fix security bugs?
H. Pearce, B. Tan, B. Ahmad, R. Karri, and B. Dolan-Gavitt · 2021
Cited alongside, same era.
Ml-enhanced code completion improves developer productivity, Jul 2022
M. T. Tabachnyk and S. Nikolov · 2022
Closest in time.
Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
P. Vaithilingam, T. Zhang, and E. L. Glassman · 2022
Closest in time.
Productivity assessment of neural code completion
A. Ziegler, E. Kalliamvakou, X. A. Li, A. Rice, D. Rifkin, S. Simister, G. Sittampalam, and E. Aftandilian · 2022
Closest in time.
Is github’s copilot as bad as humans at introducing vulnerabilities in code?
O. Asare, M. Nagappan, and N. Asokan · 2023
Closest in time.
Grounded copilot: How programmers interact with code-generating models
S. Barke, M. B. James, and N. Polikarpova · 2023
Closest in time.
Github copilot ai pair programmer: Asset or liability?
A. M. Dakhel, V. Majdinasab, A. Nikanjam, F. Khomh, M. C. Desmarais, and Z. M. J. Jiang · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Perfection not required? human-ai partnerships in code translation
J. D. Weisz, M. Muller, S. Houde, J. Richards, S. I. Ross, F. Martinez, M. Agarwal, and K. Talamadupula · 2021
Cited alongside, same era.
Ml-powered coding companion – amazon codewhisperer, 2022
Amazon · 2022
Cited alongside, same era.
Github copilot - your ai pair programmer, 2022
Github · 2022
Cited alongside, same era.
Discovering the syntax and strategies of natural language programming with generative language models
E. Jiang, E. Toh, A. Molina, K. Olson, C. Kayacik, A. Donsbach, C. J. Cai, and M. Terry · 2022
Cited alongside, same era.
Research: Quantifying github copilot’s impact on developer productivity and happiness, Sep 2022
E. Kalliamvakou · 2022
Cited alongside, same era.
Competition-level code generation with alphacode
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. D. Lago, et al · 2022
Cited alongside, same era.
Asleep at the keyboard? assessing the security of github copilot’s code contributions
H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri · 2022
Cited alongside, same era.
Closest in time.
Out of the bleu: how should we assess quality of the code generation models?
M. Evtikhiev, E. Bogomolov, Y. Sokolov, and T. Bryksin · 2023
Closest in time.
Understanding the usability of ai programming assistants
J. T. Liang, C. Yang, and B. A. Myers · 2023
Closest in time.
The impact of ai on developer productivity: Evidence from github copilot
S. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer · 2023
Closest in time.
J. Prather, B. N. Reeves, P. Denny, B. A. Becker, J. Leinonen, A. Luxton-Reilly, G. Powell, J. Finnie-Ansley, and E. A. Santos · 2023
Closest in time.
Don’t complete it! preventing unhelpful code completion for productive and sustainable neural code completion systems
Z. Sun, X. Du, F. Song, S. Wang, M. Ni, and L. Li · 2023
Closest in time.
Is ai the better programming partner? human-human pair programming vs. human-ai pair programming
T. Wu, K. Koedinger, et al · 2023
Closest in time.