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Recent advances in Large Language Models (LLM) have made automatic code generation possible for real-world programming tasks in general-purpose programming languages such as Python. However, there are few human studies on the usability of these tools and how they fit the programming workflow. In this work, we conducted a within-subjects user study with 24 participants to understand how programmers use and perceive Copilot, a LLM-based code generation tool. We found that, while Copilot did not necessarily improve the task completion time or success rate, most participants preferred to use Copilot in daily programming tasks, since Copilot often provided a useful starting point and saved the effort of searching online. However, participants did face difficulties in understanding, editing, and debugging code snippets generated by Copilot, which significantly hindered their task-solving effectiveness. Finally, we highlighted several promising directions for improving the design of Copilot based on our observations and participants’ feedback.
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Human-Centric Programming in the Large - Command Languages to Scalable Cyber Training
Programming in the large allows composition of processes executing code written using programming in the small. Traditionally, systems supporting programming in the large have included interpreters of OS command languages, but today, with the emergence of collaborative “big data” science, these systems also include cyberinfrastructures, which allow computations to be carried out on remote machines in the “cloud”. The rationale for these systems, even the traditional command interpreters, is human-centric computing, as they are designed to support quick, interactive development and execution of process workflows. Some cyberinfrastructures extend this human-centricity by also providing manipulation of visualizations of these workflows. To further increase the human-centricity of these systems, we have started a new project on cyber training - instruction in the use of command languages and visual components of cyberinfrastructures. Our objective is to provide scalable remote awareness of trainees' progress and difficulties, as well as collaborative and automatic resolution of their difficulties. Our current plan is to provide awareness based on a subway workflow metaphor, allow a trainer to collaborate with multiple trainees using a single instance of a command interpreter, and combine research in process and interaction workflows to support automatic help. These research directions can be considered an application of the general principle of integrating programming in the small and large.
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- Award ID(s):
- 1829752
- PAR ID:
- 10104308
- Date Published:
- Journal Name:
- 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
- Page Range / eLocation ID:
- 295 to 297
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VLHCC.2018.8506564
