Comprehensive Evaluation of AI-Powered Programming Assistants: Assessing Effectiveness, Characteristics, and User Experiences

Khái niệm cốt lõi

AI-powered programming assistants can enhance task completion rates, reduce time, improve code quality, and increase self-perceived productivity, but their impact varies across different tasks and user experience levels.

Tóm tắt

The study simulates real software development scenarios and conducts a controlled human study with 27 computer science students to comprehensively evaluate the effectiveness and usability of three popular AI coding assistant tools (ACATs): GitHub Copilot, Tabnine, and CodeGeeX.
The key findings include:

ACATs generally increase task completion rates, resulting in a general reduction in task completion time. However, for highly experienced users, ACATs may even increase completion time.

ACATs enhance code quality, with GitHub Copilot demonstrating the most outstanding performance. The "Management System Development" task is most affected by ACATs across multiple dimensions.

The acceptance rate of ACATs' code recommendations varies, peaking at 40% for the "Management System Development" task. CodeGeeX has the highest acceptance rate at 49%, while Tabnine has the lowest at 24%.

Over 55% of participants feel programming more strenuous without ACAT's help, indicating ACATs improve participants' self-perceived productivity.

Regarding recommended code characteristics, "edited line completion" is the most frequent recommendation, while "comments completion" and "string completion" have the lowest acceptance rates.

Participants' decisions to accept, reject, or modify ACAT recommendations are influenced by 22 factors, including fulfilling functional requirements, reducing keystrokes, and addressing logical errors.

Participants face challenges with ACATs, such as poor performance on complex logic, limited natural language understanding, and varying preferences on code recommendation length. They also express 23 expectations, including enhanced natural language interaction, optimized ranking of suggestions, and adaptive learning of personal coding styles.

The findings provide valuable insights to inform the further improvement of AI-powered programming assistants and enhance their support for developers.

Thống kê

"ACATs generally enhance task completion rates, resulting in a general reduction in task completion time."
"For highly experienced users, ACATs may even increase completion time."
"GitHub Copilot brings the largest code quality score difference (1.61, ↑54%)."
"The acceptance rate of ACATs' code recommendations peaks at 40% for the 'Management System Development' task."
"Over 55% of participants feel programming more strenuous without ACAT's help."

Trích dẫn

"I accept this because it's a long code, it even has most of the structure already written for me, which saved me a lot of time."
"ACAT misunderstood his intention, as he intended to complete the code based on his comments, but the ACAT instead completed his comments."

Thông tin chi tiết chính được chắt lọc từ

How far are AI-powered programming assistants from meeting developers' needs?

by Xin Tan,Xiao... lúc arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12000.pdf

Yêu cầu sâu hơn

How can ACATs be further improved to better support developers with complex logic tasks?

To better support developers with complex logic tasks, ACATs can be improved in several ways:

Enhanced Natural Language Understanding: ACATs should improve their natural language understanding capabilities to accurately interpret and generate code based on complex logic requirements expressed in natural language. This would enable developers to convey intricate logic concepts more effectively to the ACAT.

Advanced Algorithm Training: ACATs can benefit from advanced algorithm training on a wider range of complex logic scenarios. By exposing the AI models to diverse and challenging logic problems, they can better understand and generate code for complex tasks.

Contextual Awareness: ACATs should be designed to have a deeper understanding of the context in which the code is being written. This includes recognizing patterns, dependencies, and constraints specific to complex logic tasks, allowing for more accurate and relevant code suggestions.

Multi-Modal Support: Introducing multi-modal support, such as incorporating visual aids or diagrams alongside code suggestions, can help developers better comprehend and tackle complex logic tasks. This visual representation can enhance the understanding of intricate logic structures.

Feedback Mechanism: Implementing a feedback mechanism where developers can provide input on the quality and relevance of code suggestions for complex logic tasks can help ACATs learn and improve over time. This iterative process of feedback and learning can enhance the AI's performance in handling complex logic scenarios.

How can the integration of ACATs with other development tools and workflows be enhanced to create a more seamless and efficient programming experience?

To create a more seamless and efficient programming experience through the integration of ACATs with other development tools and workflows, the following strategies can be implemented:

Unified Development Environment: Integrating ACATs seamlessly into popular Integrated Development Environments (IDEs) like Visual Studio Code, IntelliJ IDEA, or Eclipse can enhance the developer experience. This integration should provide a cohesive interface where developers can access ACAT features without switching between multiple tools.

Compatibility with Version Control Systems: Ensuring compatibility with version control systems like Git allows developers to seamlessly manage code changes and collaborate with team members. ACATs should support features that facilitate code versioning, branching, and merging within the development workflow.

API Integration: Integrating ACATs with popular APIs and libraries used in software development can enhance code suggestions and recommendations. By leveraging external APIs for specific functionalities, ACATs can provide more tailored and context-aware code assistance to developers.

Task Automation: ACATs can be integrated with task automation tools like Jenkins or Travis CI to automate repetitive tasks, code reviews, and testing processes. This integration streamlines the development workflow, allowing developers to focus on high-level problem-solving rather than mundane tasks.

Collaboration Tools Integration: Integrating ACATs with collaboration tools like Slack or Microsoft Teams enables seamless communication and code sharing among team members. Developers can easily discuss code suggestions, share snippets, and collaborate on projects within the same platform.

By enhancing the integration of ACATs with these tools and workflows, developers can experience a more streamlined and efficient programming process, ultimately improving productivity and code quality.