Assessing AI-Based Code Assistants in Method Generation Tasks
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Abstract (arXiv)
AI-based code assistants are increasingly popular as a means to enhance productivity and improve code quality. This study compares four AI-based code assistants, GitHub Copilot, Tabnine, ChatGPT, and Google Bard, in method generation tasks, assessing their ability to produce accurate, correct, and efficient code. Results show that code assistants are useful, with complementary capabilities, although they rarely generate ready-to-use correct code.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Feb 2024.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025AdaCoder: An Adaptive Planning and Multi-Agent Framework for Function-Level Code Generationcited by 8
- 2024Disrupting Test Development with AI Assistantscited by 0
- 2025Paradigm shift on Coding Productivity Using GenAIcited by 0
- 2025Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agentscited by 0
- 2026Generation of Web Apps with Agentic IDEs: An Empirical Assessmentcited by 0
- 2026Generation of Web Apps with Agentic IDEs: An Empirical Assessment21 Aug 2026
- 2025Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents24 Jun 2025
- 2025Paradigm shift on Coding Productivity Using GenAI25 Apr 2025
- 2025AdaCoder: An Adaptive Planning and Multi-Agent Framework for Function-Level Code Generation5 Apr 2025
- 2024Disrupting Test Development with AI Assistants4 Nov 2024