WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911106959671296 |
|---|---|
| author | Kolhatkar, Dhruv Akkena, Soubhagya Gehringer, Edward F. |
| author_facet | Kolhatkar, Dhruv Akkena, Soubhagya Gehringer, Edward F. |
| contents | This work-in-progress research-to-practice paper explores the integration of Large Language Models (LLMs) into the code-review process for open-source software projects developed in computer science and software engineering courses. The focus is on developing an automated feedback tool that evaluates student code for adherence to key object-oriented design principles, addressing the need for more effective and scalable methods to teach software design best practices. The innovative practice involves leveraging LLMs and Retrieval-Augmented Generation (RAG) to create an automated feedback system that assesses student code for principles like SOLID, DRY, and design patterns. It analyzes the effectiveness of various prompting strategies and the RAG integration. Preliminary findings show promising improvements in code quality. Future work will aim to improve model accuracy and expand support for additional design principles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11717 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG Kolhatkar, Dhruv Akkena, Soubhagya Gehringer, Edward F. Software Engineering This work-in-progress research-to-practice paper explores the integration of Large Language Models (LLMs) into the code-review process for open-source software projects developed in computer science and software engineering courses. The focus is on developing an automated feedback tool that evaluates student code for adherence to key object-oriented design principles, addressing the need for more effective and scalable methods to teach software design best practices. The innovative practice involves leveraging LLMs and Retrieval-Augmented Generation (RAG) to create an automated feedback system that assesses student code for principles like SOLID, DRY, and design patterns. It analyzes the effectiveness of various prompting strategies and the RAG integration. Preliminary findings show promising improvements in code quality. Future work will aim to improve model accuracy and expand support for additional design principles. |
| title | WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2508.11717 |