Generating Complex Code Analyzers from Natural Language Questions
Fuente:
arXiv
Guardado en:
| Autores principales: | Nazari, Amirmohammad, Sabouri, Sadra, Zhu, Wang Bill, Jia, Robin, Chattopadhyay, Souti, Raghothaman, Mukund |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Cognitive Biases in LLM-Assisted Software Development
por: Zhou, Xinyi, et al.
Publicado: (2026)
por: Zhou, Xinyi, et al.
Publicado: (2026)
Exploring the Challenges and Opportunities of AI-assisted Codebase Generation
por: Eibl, Philipp, et al.
Publicado: (2025)
por: Eibl, Philipp, et al.
Publicado: (2025)
NaturalEdit: Code Modification through Direct Interaction with Adaptive Natural Language Representation
por: Tang, Ningzhi, et al.
Publicado: (2025)
por: Tang, Ningzhi, et al.
Publicado: (2025)
Auditing and Controlling AI Agent Actions in Spreadsheets
por: Sabouri, Sadra, et al.
Publicado: (2026)
por: Sabouri, Sadra, et al.
Publicado: (2026)
Investigating and Designing for Trust in AI-powered Code Generation Tools
por: Wang, Ruotong, et al.
Publicado: (2023)
por: Wang, Ruotong, et al.
Publicado: (2023)
A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions
por: Tang, Ningzhi, et al.
Publicado: (2024)
por: Tang, Ningzhi, et al.
Publicado: (2024)
"I Would Have Written My Code Differently'': Beginners Struggle to Understand LLM-Generated Code
por: Zi, Yangtian, et al.
Publicado: (2025)
por: Zi, Yangtian, et al.
Publicado: (2025)
Patterns of Developer Adoption of LLM-Generated Code Refactoring Suggestions
por: Schön, David, et al.
Publicado: (2026)
por: Schön, David, et al.
Publicado: (2026)
Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
por: Tang, Ningzhi, et al.
Publicado: (2025)
por: Tang, Ningzhi, et al.
Publicado: (2025)
What's in a Proof? Analyzing Expert Proof-Writing Processes in F* and Verus
por: Jain, Rijul, et al.
Publicado: (2025)
por: Jain, Rijul, et al.
Publicado: (2025)
From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions
por: Alfageeh, Ali, et al.
Publicado: (2025)
por: Alfageeh, Ali, et al.
Publicado: (2025)
Diversity's Double-Edged Sword: Analyzing Race's Effect on Remote Pair Programming Interactions
por: Mason, Shandler A., et al.
Publicado: (2024)
por: Mason, Shandler A., et al.
Publicado: (2024)
ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations
por: Joshi, Brihi, et al.
Publicado: (2025)
por: Joshi, Brihi, et al.
Publicado: (2025)
CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development
por: Mowar, Peya, et al.
Publicado: (2025)
por: Mowar, Peya, et al.
Publicado: (2025)
Gendered Prompting and LLM Code Review: How Gender Cues in the Prompt Shape Code Quality and Evaluation
por: Janzen, Lynn, et al.
Publicado: (2026)
por: Janzen, Lynn, et al.
Publicado: (2026)
Bridging the Interpretation Gap in Accessibility Testing: Empathetic and Legal-Aware Bug Report Generation via Large Language Models
por: Koyama, Ryoya, et al.
Publicado: (2026)
por: Koyama, Ryoya, et al.
Publicado: (2026)
UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback
por: Wu, Jason, et al.
Publicado: (2024)
por: Wu, Jason, et al.
Publicado: (2024)
Automatic Bias Detection in Source Code Review
por: Alebachew, Yoseph Berhanu, et al.
Publicado: (2025)
por: Alebachew, Yoseph Berhanu, et al.
Publicado: (2025)
Assessing Consensus of Developers' Views on Code Readability
por: Sergeyuk, Agnia, et al.
Publicado: (2024)
por: Sergeyuk, Agnia, et al.
Publicado: (2024)
"Always Nice and Confident, Sometimes Wrong": Developer's Experiences Engaging Large Language Models (LLMs) Versus Human-Powered Q&A Platforms for Coding Support
por: Li, Jiachen, et al.
Publicado: (2023)
por: Li, Jiachen, et al.
Publicado: (2023)
SPROUT: an Interactive Authoring Tool for Generating Programming Tutorials with the Visualization of Large Language Models
por: Liu, Yihan, et al.
Publicado: (2023)
por: Liu, Yihan, et al.
Publicado: (2023)
Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?
por: Kou, Bonan, et al.
Publicado: (2023)
por: Kou, Bonan, et al.
Publicado: (2023)
Detecting UX smells in Visual Studio Code using LLMs
por: Rodriguez, Andrés, et al.
Publicado: (2026)
por: Rodriguez, Andrés, et al.
Publicado: (2026)
Code Compass: A Study on the Challenges of Navigating Unfamiliar Codebases
por: Agrawal, Ekansh, et al.
Publicado: (2024)
por: Agrawal, Ekansh, et al.
Publicado: (2024)
A Low-Code Approach for the Automatic Personalization of Conversational Agents
por: Conrardy, Aaron, et al.
Publicado: (2026)
por: Conrardy, Aaron, et al.
Publicado: (2026)
Evaluating the Quality of Code Comments Generated by Large Language Models for Novice Programmers
por: Fan, Aysa Xuemo, et al.
Publicado: (2024)
por: Fan, Aysa Xuemo, et al.
Publicado: (2024)
Reassessing Java Code Readability Models with a Human-Centered Approach
por: Sergeyuk, Agnia, et al.
Publicado: (2024)
por: Sergeyuk, Agnia, et al.
Publicado: (2024)
Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency
por: Thorgeirsson, Sverrir, et al.
Publicado: (2026)
por: Thorgeirsson, Sverrir, et al.
Publicado: (2026)
Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping
por: Xiao, Jingyu, et al.
Publicado: (2024)
por: Xiao, Jingyu, et al.
Publicado: (2024)
MLLM-Based UI2Code Automation Guided by UI Layout Information
por: Wu, Fan, et al.
Publicado: (2025)
por: Wu, Fan, et al.
Publicado: (2025)
Vibe Coding: Toward an AI-Native Paradigm for Semantic and Intent-Driven Programming
por: Bamil, Vinay
Publicado: (2025)
por: Bamil, Vinay
Publicado: (2025)
40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study
por: Wyrich, Marvin, et al.
Publicado: (2022)
por: Wyrich, Marvin, et al.
Publicado: (2022)
Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise
por: Weisz, Justin D., et al.
Publicado: (2024)
por: Weisz, Justin D., et al.
Publicado: (2024)
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
por: Zhang, Yifan, et al.
Publicado: (2025)
por: Zhang, Yifan, et al.
Publicado: (2025)
Emotional Contagion in Code: How GitHub Emoji Reactions Shape Developer Collaboration
por: Kraishan, Obada
Publicado: (2025)
por: Kraishan, Obada
Publicado: (2025)
From Teacher to Colleague: How Coding Experience Shapes Developer Perceptions of AI Tools
por: Zakharov, Ilya, et al.
Publicado: (2025)
por: Zakharov, Ilya, et al.
Publicado: (2025)
Exit the Code: A Model for Understanding Career Abandonment Intention Among Software Developers
por: Massoni, Tiago, et al.
Publicado: (2025)
por: Massoni, Tiago, et al.
Publicado: (2025)
CodeAlignBench: Assessing Code Generation Models on Developer-Preferred Code Adjustments
por: Mehralian, Forough, et al.
Publicado: (2025)
por: Mehralian, Forough, et al.
Publicado: (2025)
GazeCopilot: Evaluating Novel Gaze-Informed Prompting for AI-Supported Code Comprehension and Readability
por: Elfares, Yasmine, et al.
Publicado: (2025)
por: Elfares, Yasmine, et al.
Publicado: (2025)
GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant
por: Merten, Gaspard, et al.
Publicado: (2025)
por: Merten, Gaspard, et al.
Publicado: (2025)
Ejemplares similares
-
Cognitive Biases in LLM-Assisted Software Development
por: Zhou, Xinyi, et al.
Publicado: (2026) -
Exploring the Challenges and Opportunities of AI-assisted Codebase Generation
por: Eibl, Philipp, et al.
Publicado: (2025) -
NaturalEdit: Code Modification through Direct Interaction with Adaptive Natural Language Representation
por: Tang, Ningzhi, et al.
Publicado: (2025) -
Auditing and Controlling AI Agent Actions in Spreadsheets
por: Sabouri, Sadra, et al.
Publicado: (2026) -
Investigating and Designing for Trust in AI-powered Code Generation Tools
por: Wang, Ruotong, et al.
Publicado: (2023)