The Shift from Writing to Pruning Software: A Bonsai-Inspired IDE for Reshaping AI Generated Code

• URL: http://arxiv.org/abs/2503.02833v1
• Date: Tue, 04 Mar 2025 17:57:26 GMT
• Title: The Shift from Writing to Pruning Software: A Bonsai-Inspired IDE for Reshaping AI Generated Code
• Authors: Raula Gaikovina Kula, Christoph Treude,
• Abstract summary: The rise of AI-driven coding assistants signals a fundamental shift in how software is built.<n>While AI coding assistants have been integrated into existing Integrated Development Environments, their full potential remains largely untapped.<n>We propose a new approach to IDEs, where AI is allowed to generate in its true, unconstrained form, free from traditional file structures.
• Score: 11.149764135999437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of AI-driven coding assistants signals a fundamental shift in how software is built. While AI coding assistants have been integrated into existing Integrated Development Environments (IDEs), their full potential remains largely untapped. A key challenge is that these AI assistants can suffer from hallucinations, leading developers down decision paths that the AI should not dictate, sometimes even without the users awareness or consent. Moreover, current static-file IDEs lack the mechanisms to address critical issues such as tracking the provenance of AI-generated code and integrating version control in a way that aligns with the dynamic nature of AI-assisted development. As a result, developers are left without the necessary tools to manage, refine, and validate AI generated code systematically, making it difficult to ensure correctness, maintainability, and trust in the development process. Existing IDEs treat AI-generated code as static text, offering limited support for managing its evolution, refinement, or multiple alternative paths. Drawing inspiration from the ancient art of Japanese Bonsai gardening focused on balance, structure, and deliberate pruning: we propose a new approach to IDEs, where AI is allowed to generate in its true, unconstrained form, free from traditional file structures. This approach fosters a more fluid and interactive method for code evolution. We introduce the concept of a Bonsai-inspired IDE, structured as a graph of generated code snippets and multiple code paths, enabling developers to reshape AI generated code to suit their needs. Our vision calls for a shift away from a static file based model toward a dynamic, evolving system that allows for continuous refinement of generated code, with the IDE evolving alongside AI powered modifications rather than merely serving as a place to write and edit code.

Related papers

• On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices [2.655152359733829]
  This study aims to understand the ways developers use to self-declare AI-generated code. We collected 613 instances of AI-generated code snippets from GitHub. Our research revealed the practices followed by developers to self-declare AI-generated code.
  arXiv  Detail & Related papers  (2025-04-23T07:52:39Z)
• Explainability for Embedding AI: Aspirations and Actuality [1.8130068086063336]
  Explainable AI (XAI) may allow developers to understand better the systems they build. Existing XAI systems still fall short of this aspiration. We see an unmet need to provide developers with adequate support mechanisms to cope with this complexity.
  arXiv  Detail & Related papers  (2025-04-20T14:20:01Z)
• A New Generation of Intelligent Development Environments [0.0]
  The practice of programming is undergoing a revolution with the introduction of AI assisted development (copilots) and the creation of new programming languages. This paper presents a vision for transforming the Integrated Development Environment from an Integrated Development Environment to an Intelligent Development Environment.
  arXiv  Detail & Related papers  (2024-06-13T20:33:25Z)
• From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030 [3.437372707846067]
  We provide a comparative analysis between the current state of AI-assisted programming in 2024 and our projections for 2030. We envision HyperAssistant, an augmented AI tool that offers comprehensive support to 2030 developers.
  arXiv  Detail & Related papers  (2024-05-21T12:37:36Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• Exploring the intersection of Generative AI and Software Development [0.0]
  The synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
  arXiv  Detail & Related papers  (2023-12-21T19:23:23Z)
• Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs [0.0]
  The mainstream AIs approaches are the generative and deep learning approaches with large language models (LLMs) and the manually constructed symbolic approach. This position paper lays out the prospects, gaps, and challenges for extending the practice of developmental AIs to create resilient, intelligent, and human-compatible AIs.
  arXiv  Detail & Related papers  (2023-08-08T21:14:21Z)
• Guiding AI-Generated Digital Content with Wireless Perception [69.51950037942518]
  We introduce an integration of wireless perception with AI-generated content (AIGC) to improve the quality of digital content production. The framework employs a novel multi-scale perception technology to read user's posture, which is difficult to describe accurately in words, and transmits it to the AIGC model as skeleton images. Since the production process imposes the user's posture as a constraint on the AIGC model, it makes the generated content more aligned with the user's requirements.
  arXiv  Detail & Related papers  (2023-03-26T04:39:03Z)
• 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)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Data-Driven and SE-assisted AI Model Signal-Awareness Enhancement and Introspection [61.571331422347875]
  We propose a data-driven approach to enhance models' signal-awareness. We combine the SE concept of code complexity with the AI technique of curriculum learning. We achieve up to 4.8x improvement in model signal awareness.
  arXiv  Detail & Related papers  (2021-11-10T17:58:18Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)

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.