mlsanityData to 14 Oct 2026

AI for Low-Code for AI

arXiv 2305.20015 ↗First posted 31 May 2023cs.SE cs.AIcited by 3

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Abstract (arXiv)

Low-code programming allows citizen developers to create programs with minimal coding effort, typically via visual (e.g. drag-and-drop) interfaces. In parallel, recent AI-powered tools such as Copilot and ChatGPT generate programs from natural language instructions. We argue that these modalities are complementary: tools like ChatGPT greatly reduce the need to memorize large APIs but still require their users to read (and modify) programs, whereas visual tools abstract away most or all programming but struggle to provide easy access to large APIs. At their intersection, we propose LowCoder, the first low-code tool for developing AI pipelines that supports both a visual programming interface (LowCoder_VP) and an AI-powered natural language interface (LowCoder_NL). We leverage this tool to provide some of the first insights into whether and how these two modalities help programmers by conducting a user study. We task 20 developers with varying levels of AI expertise with implementing four ML pipelines using LowCoder, replacing the LowCoder_NL component with a simple keyword search in half the tasks. Overall, we find that LowCoder is especially useful for (i) Discoverability: using LowCoder_NL, participants discovered new operators in 75% of the tasks, compared to just 32.5% and 27.5% using web search or scrolling through options respectively in the keyword-search condition, and (ii) Iterative Composition: 82.5% of tasks were successfully completed and many initial pipelines were further successfully improved. Qualitative analysis shows that AI helps users discover how to implement constructs when they know what to do, but still fails to support novices when they lack clarity on what they want to accomplish. Overall, our work highlights the benefits of combining the power of AI with low-code programming.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 53 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

12 of 53 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  5. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  6. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 744
  7. 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 654
  8. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  9. 2021In-IDE Code Generation from Natural Language: Promise and Challengescited by 42
  10. 2015Program Synthesis using Natural Languagecited by 41
  11. 2019Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrationscited by 26
  12. 2022Low-Code Programming Modelscited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  2. 2025Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMscited by 2
  3. 2025Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphscited by 1