Developer Insights on Usability Challenges of AI-Assisted Programming Tools
Developers face usability challenges with AI programming tools like GitHub Copilot, impacting productivity and code assessment.
Key Points
- • AI-assisted tools are popular but present usability issues.
- • Users prefer AI for initial code generation but struggle with code correctness.
- • Acceptance of AI suggestions impacts perceived productivity.
- • Interactivity modes include acceleration and exploration.
Recent studies have illuminated the complex usability challenges facing developers who use AI-assisted programming tools, particularly GitHub Copilot. While these tools gain traction for their ability to generate code suggestions, user experiences remain mixed, necessitating deeper analysis of their effectiveness and usability.
A comprehensive user study highlighted that although developers preferred Copilot for its potential to provide useful starting points, they encountered difficulties in assessing the correctness of the AI-generated code. A comparative study by Vaithilingam et al. (2022) found that participants using Copilot struggled more with task completion compared to traditional tools like Intellisense.
Moreover, research led by Ziegler et al. (2022) revealed that the acceptance of AI suggestions strongly correlated with perceived productivity, and users were more inclined to embrace these AI inputs during less busy hours. Conversely, Xu et al. (2022) identified that ambiguities in user queries significantly hindered the generation of accurate code, contributing to developer frustration.
Additionally, a study by Barke et al. (2022) distinguished between two primary modes of interaction with Copilot: acceleration, aimed at speeding up coding tasks, and exploration for structuring and planning. Despite the potential benefits, many users reported only neutral or mildly positive experiences with these tools, suggesting that significant adjustments and enhanced usability are essential for maximizing their effectiveness.