Table of Links
2. Contexts, Methods, and Tasks
3.1. Quality and 3.2 Productivity
5. Discussion and Future Work
5.1. LLM, Your pAIr Programmer?
5.2. LLM, A Better pAIr Programmer?
5.3. LLM, Students’ pAIr Programmer?
6. Conclusion, Acknowledgments, and References
4.5 Logistics
Logistical challenges, including scheduling difficulties, teaching and evaluating collaboration for the pair, and figuring out individual accountability and responsibility [11, 67], can add to the management cost of human-human pair programming [4, 79].
In human-AI pair programming, some may argue that the human is solely responsible in the human-AI pair [72], but the accountability of these LLM-based generative AI is still under debate [10]. There may be new logistics issues for the human-AI pair, such as teaching humans how to best collaborate with Copilot. There could also be unique challenges as in every human-AI interaction scenario, such as bias, trust, and technical limitations – much to be explored. More study would be needed to empirically and experimentally verify the moderating effects of different variables in human-AI pair programming.
Summary: Human-human pair programming literature have found moderators including task type & complexity, compatibility, communication, collaboration, and logistics. However, there is a lack of in-depth examination of potential moderating effects in current pAIr works.
Authors:
(1) Qianou Ma (Corresponding author), Carnegie Mellon University, Pittsburgh, USA ([email protected]);
(2) Tongshuang Wu, Carnegie Mellon University, Pittsburgh, USA ([email protected]);
(3) Kenneth Koedinger, Carnegie Mellon University, Pittsburgh, USA ([email protected]).
This paper is
