Practitioners' Expectations on Code Completion
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Abstract (arXiv)
Code completion has become a common practice for programmers during their daily programming activities. It aims at automatically predicting the next tokens or lines that the programmers tend to use. A good code completion tool can substantially save keystrokes and improve the programming efficiency for programmers. Recently, various techniques for code completion have been proposed for usage in practice. However, it is still unclear what are practitioners' expectations on code completion and whether existing research has met their demands. To fill the gap, we perform an empirical study by first interviewing 15 practitioners and then surveying 599 practitioners from 18 IT companies about their expectations on code completion. We then compare the practitioners' demands with current research via conducting a literature review of papers on code completion published in premier publication venues from 2012 to 2022. Based on the comparison, we highlight the directions desirable for researchers to invest efforts towards developing code completion techniques for meeting practitioners' expectations.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jan 2023.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 1.
8 papers.
- 2024Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forwardcited by 45
- 2024REPOFUSE: Repository-Level Code Completion with Fused Dual Contextcited by 32
- 2023Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmapscited by 19
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
- 2025Enhancing Project-Specific Code Completion by Inferring Internal API Informationcited by 5
- 2024Full Line Code Completion: Bringing AI to Desktopcited by 4
- 2025TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEscited by 0
- 2026Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineeringcited by 0
- 2026Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering8 Jun 2026
- 2025TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs4 Aug 2025
- 2025Enhancing Project-Specific Code Completion by Inferring Internal API Information28 Jul 2025
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Review8 Mar 2025
- 2024Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward11 Jun 2024
- 2024Full Line Code Completion: Bringing AI to Desktop14 May 2024
- 2024REPOFUSE: Repository-Level Code Completion with Fused Dual Context22 Feb 2024
- 2023Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps8 Sep 2023