Image de couverture de livre

Beyond AI Delegation: A Prompt Pattern Framework for Productive Struggle and Evaluative Judgement in Secure Coding Education

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Haindl, Philipp, Eigner, Oliver, Kieseberg, Peter
Format:	Preprint
Publié:	2026
Sujets:	Software Engineering
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Blockchain-based PKI within a Corporate Organization: Advantages and Challenges
par: Springer, Julian, et autres
Publié: (2024)

Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants
par: Chen, Valerie, et autres
Publié: (2026)

Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration
par: DiCuffa, Sophia, et autres
Publié: (2025)

iCodeReviewer: Improving Secure Code Review with Mixture of Prompts
par: Peng, Yun, et autres
Publié: (2025)

Product Manager Practices for Delegating Work to Generative AI: "Accountability must not be delegated to non-human actors"
par: Ulloa, Mara, et autres
Publié: (2025)

Codexity: Secure AI-assisted Code Generation
par: Kim, Sung Yong, et autres
Publié: (2024)

Secure Coding with AI -- From Detection to Repair
par: Belozerov, Vladislav, et autres
Publié: (2025)

Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement
par: Jin, Haolin, et autres
Publié: (2026)

Enhancing Software Testing Education: Understanding Where Students Struggle
par: Andleeb, Shiza, et autres
Publié: (2025)

DeVAIC: A Tool for Security Assessment of AI-generated Code
par: Cotroneo, Domenico, et autres
Publié: (2024)

Evaluating Large Language Models for Code Translation: Effects of Prompt Language and Prompt Design
par: Aljagthami, Aamer, et autres
Publié: (2025)

Effective Delegation and Leadership in Software Management
par: Mirkhan, Star Dawood, et autres
Publié: (2024)

Mining Type Constructs Using Patterns in AI-Generated Code
par: Lee, Imgyeong, et autres
Publié: (2026)

A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
par: Lian, Keke, et autres
Publié: (2025)

LLM-as-a-Judge for Human-AI Co-Creation: A Reliability-Aware Evaluation Framework for Coding
par: Amin, Md Faizul Ibne, et autres
Publié: (2026)

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation
par: Mamun, Md Afif Al, et autres
Publié: (2026)

How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test
par: Tessa, Melissa, et autres
Publié: (2026)

The Role of Generative AI in Strengthening Secure Software Coding Practices: A Systematic Perspective
par: Alwageed, Hathal S., et autres
Publié: (2025)

CFCEval: Evaluating Security Aspects in Code Generated by Large Language Models
par: Cheng, Cheng, et autres
Publié: (2025)

Beyond Isolated Tasks: A Framework for Evaluating Coding Agents on Sequential Software Evolution
par: Shastry, KN Ajay, et autres
Publié: (2026)

Cracking the Code: Evaluating Zero-Shot Prompting Methods for Providing Programming Feedback
par: Ippisch, Niklas, et autres
Publié: (2024)

Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval
par: Wang, Jiexin, et autres
Publié: (2024)

From Struggle to Simplicity with a Usable and Secure API for Encryption in Java
par: Firouzi, Ehsan, et autres
Publié: (2024)

The Readability Spectrum: Patterns, Issues, and Prompt Effects in LLM-Generated Code
par: Ye, Hengzhi, et autres
Publié: (2026)

On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies
par: Jahromi, Ali Soltanian Fard, et autres
Publié: (2026)

jscefr: A Framework to Evaluate the Code Proficiency for JavaScript
par: Ragkhitwetsagul, Chaiyong, et autres
Publié: (2024)

Production-Grade AI Coding System for Client-Side Development
par: Wang, Ruihan, et autres
Publié: (2026)

Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses
par: Charoenwet, Wachiraphan, et autres
Publié: (2023)

Do Prompt Patterns Affect Code Quality? A First Empirical Assessment of ChatGPT-Generated Code
par: Della Porta, Antonio, et autres
Publié: (2025)

ParaStudent: Generating and Evaluating Realistic Student Code by Teaching LLMs to Struggle
par: Miroyan, Mihran, et autres
Publié: (2025)

Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation
par: Ye, Sixiang, et autres
Publié: (2025)

On the Quality of AI-Generated Source Code Comments: A Comprehensive Evaluation
par: Guelman, Ian, et autres
Publié: (2024)

Generative AI in Systems Engineering: A Framework for Risk Assessment of Large Language Models
par: Otten, Stefan, et autres
Publié: (2026)

AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software
par: Wang, Bin, et autres
Publié: (2025)

Where Do LLMs Still Struggle? An In-Depth Analysis of Code Generation Benchmarks
par: Sharifloo, Amir Molzam, et autres
Publié: (2025)

Struggling to Connect: A Researchers' Reflection on Networking in Software Engineering
par: Chakraborty, Shalini
Publié: (2026)

Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation
par: Yan, Hao, et autres
Publié: (2025)

Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis
par: Sabra, Abbas, et autres
Publié: (2025)

The First Prompt Counts the Most! An Evaluation of Large Language Models on Iterative Example-Based Code Generation
par: Fu, Yingjie, et autres
Publié: (2024)

Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws
par: Centellas-Claros, Leonardo, et autres
Publié: (2025)