Will Code Remain a Relevant User Interface for End-User Programming with
Generative AI Models?
- URL: http://arxiv.org/abs/2311.00382v1
- Date: Wed, 1 Nov 2023 09:20:21 GMT
- Title: Will Code Remain a Relevant User Interface for End-User Programming with
Generative AI Models?
- Authors: Advait Sarkar
- Abstract summary: We explore the extent to which "traditional" programming languages remain relevant for non-expert end-user programmers in a world with generative AI.
We outline some reasons that traditional programming languages may still be relevant and useful for end-user programmers.
- Score: 20.275891144535258
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The research field of end-user programming has largely been concerned with
helping non-experts learn to code sufficiently well in order to achieve their
tasks. Generative AI stands to obviate this entirely by allowing users to
generate code from naturalistic language prompts. In this essay, we explore the
extent to which "traditional" programming languages remain relevant for
non-expert end-user programmers in a world with generative AI. We posit the
"generative shift hypothesis": that generative AI will create qualitative and
quantitative expansions in the traditional scope of end-user programming. We
outline some reasons that traditional programming languages may still be
relevant and useful for end-user programmers. We speculate whether each of
these reasons might be fundamental and enduring, or whether they may disappear
with further improvements and innovations in generative AI. Finally, we
articulate a set of implications for end-user programming research, including
the possibility of needing to revisit many well-established core concepts, such
as Ko's learning barriers and Blackwell's attention investment model.
Related papers
- Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers [0.0]
This study presents a thorough evaluation of leading programming assistants, including ChatGPT, Gemini(Bard AI), AlphaCode, and GitHub Copilot.
It emphasizes the need for ethical developmental practices to actualize AI models' full potential.
arXiv Detail & Related papers (2024-11-14T06:40:55Z) - Toward Programming Languages for Reasoning: Humans, Symbolic Systems, and AI Agents [0.0]
Integration, composition, mechanization, and AI assisted development are the driving themes in the future of software development.
This paper proposes a novel approach to this challenge -- instead of new language features or logical constructs, we propose radical simplification in the form of the Bosque platform and language.
arXiv Detail & Related papers (2024-07-08T19:50:42Z) - CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs.
CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language.
Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
arXiv Detail & Related papers (2024-05-03T02:48:55Z) - Students' Perspective on AI Code Completion: Benefits and Challenges [2.936007114555107]
We investigated the benefits, challenges, and expectations of AI code completion from students' perspectives.
Our findings show that AI code completion enhanced students' productivity and efficiency by providing correct syntax suggestions.
In the future, AI code completion should be explainable and provide best coding practices to enhance the education process.
arXiv Detail & Related papers (2023-10-31T22:41:16Z) - PwR: Exploring the Role of Representations in Conversational Programming [17.838776812138626]
We introduce Programming with Representations (PwR), an approach that uses representations to convey the system's understanding back to the user in natural language.
We find that representations significantly improve understandability, and instilled a sense of agency among our participants.
arXiv Detail & Related papers (2023-09-18T05:38:23Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - From Word Models to World Models: Translating from Natural Language to
the Probabilistic Language of Thought [124.40905824051079]
We propose rational meaning construction, a computational framework for language-informed thinking.
We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought.
We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings.
We extend our framework to integrate cognitively-motivated symbolic modules.
arXiv Detail & Related papers (2023-06-22T05:14:00Z) - Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code.
We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
arXiv Detail & Related papers (2023-02-14T18:43:34Z) - What is it like to program with artificial intelligence? [10.343988028594612]
Large language models can generate code to solve a variety of problems expressed in natural language.
This technology has already been commercialised in at least one widely-used programming editor extension: GitHub Copilot.
We explore how programming with large language models (LLM-assisted programming) is similar to, and differs from, prior conceptualisations of programmer assistance.
arXiv Detail & Related papers (2022-08-12T10:48:46Z) - Competition-Level Code Generation with AlphaCode [74.87216298566942]
We introduce AlphaCode, a system for code generation that can create novel solutions to problems that require deeper reasoning.
In simulated evaluations on recent programming competitions on the Codeforces platform, AlphaCode achieved on average a ranking of top 54.3%.
arXiv Detail & Related papers (2022-02-08T23:16:31Z) - Measuring Coding Challenge Competence With APPS [54.22600767666257]
We introduce APPS, a benchmark for code generation.
Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges.
Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
arXiv Detail & Related papers (2021-05-20T17:58:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.